[Auto-generated transcript. Edits may have been applied for clarity.] Thank you very much, all of you, for coming. I'm very, very happy, very pleased to, uh, to be introducing, uh,
our speaker tonight for the second annual, uh, Voltaire Foundation Digital Enlightenment Studies lecture.
Uh, this is something we started last year. I can't remember who the speaker was.
Um, but, uh, I think it went well. Yeah.
And that we hope to, uh, to keep going in years to come. And then we've we've coupled this with, uh, with a workshop on digital enlightenment studies.
It's been really rewarding over the past few days.
So it's really there's some momentum going and and our tonight's speaker is uh, is a big reason for that.
And so I'm happy and very pleased to, uh, to, uh, to, uh, announce Michael Dolan and professor of digital humanities at the University of Helsinki.
Uh, Mikko is, uh, is a trained intellectual historian.
And I've always been very, uh, very impressed by the way that he juggles the sort of academic rigour, uh, of, uh, of what is in many fields,
in many places, a very analogue, uh, field which is, uh, intellectual history, uh, with very serious computational explorations.
Uh, and so I think, uh, he runs, uh, the computational history, uh, group there and the University of Helsinki who does amazing experiments.
And I think he's going to talk about a few of them, if not more, uh, tonight.
So we're very pleased to have him. He's going to speak to us about, uh, books as objects, data and meaning.
Uh, thank you. Mikko. Uh, please give him a warm welcome. All right.
Um, thank you very much, Glenn. Uh, and thank you, Nicholas and everybody at the Voltaire Foundation.
This is really a great honour for me, me to do this.
And I really, uh, love to be part of what we kind of started or you started, and we try to support that as best as we can.
Um, let me say a few words about the title.
So books is objects date on meaning? Uh, it aims to capture the kind of idea that that when we think about books, uh, and in the digital realm,
uh, we want to see them to function on several different kinds of levels, which sometimes is forgotten.
And, uh, first, for us, uh, the way that we approach this is that they are physical objects, so,
so tangible artefacts that are shaped by production and circulation in many different ways.
And at the same time, uh, they are data, which is what the analogue part, uh, wasn't always thinking.
Uh, so the, the carriers or there's metadata that we can, um, mould and modify and uh,
there's textual information that can be analysed computationally.
And they are, of course, uh, vehicles of meaning, uh, which is the one that intellectual history always has been interested in.
But the approach, uh, has been everything but, uh, computational.
So, uh, cultural, uh, and intellectual expressions, uh, readers interpret and then reinterpret, uh, across different contexts.
Uh, and that's what the thing that we are interested in is, is really about.
And then let me emphasise this a computational approach.
So, so we don't want to have let's try everything.
Uh, I'll be talking how we, we sort of, uh, have had a strategy and a vision for, for some time now, already before I became a thing.
Uh, but so, so that that's something, uh, hopefully something that you get out of this talk.
So what I want to do is to kind of present this holistic approach, that what we mean,
uh, by studying books as physical objects, data and vehicles of meaning.
Uh, then I tried to offer some concrete, uh, data, uh, driven insights into enlightenment, print culture.
Uh, so if also for for those who are maybe, uh, coming from the sort of traditional side,
uh, I'm really that's the most important part that we are talking.
We are not trying to do something different. We are expanding the context, maybe, and the ways of interpretation.
But the continuum needs to be in the tradition of enlightenment studies and intellectual history.
So all of that who are not doing computational, uh, just as important as those who are, uh, experimenting with different kinds of ways.
Uh, but the point is that, uh, the methods they need to advance the scholarship also,
so that we go in some ways beyond the traditional, uh, otherwise, there's no point, uh, for, for us to do anything in this room.
And then I'll try to, uh, sketch a little bit, uh, where computational history might be heading and, uh,
how I will shape, uh, what we call the digital enlightenment studies so that those are the three objectives.
Now, uh, in the idea of, of working, uh, and giving a talk lecture, uh,
in an intellectual history, uh, and enlightenment studies, usually you would have a handout,
right, printed handout where you get a set of quotes, which kind of underlines that,
uh, the work that we do, uh, in traditional way is somewhat anecdotal.
So we are interpreting certain passages that are important.
What I want to give now, uh, through this QR code and this is now an experiment a little bit.
So, so that if you go there, uh, you will find a structure, you will find, uh, articles that embed my talk.
So there is this, uh, read number or code and you can look at which article I'm referring to every now and then.
You don't have to follow this, but it gives you kind of like a sense of where are we going.
So don't feel that you're missing out necessarily if you don't want to do that.
This gives you an opportunity for those who will want to be multitasking.
Uh, so so I get I don't get angry when if you're looking at your phone here, uh, so so so so you can do that.
But, but and and then another one is that if you're interested in what we do, you can go back,
uh, after this lecture, look at the structure, uh, and then go through those articles.
So, so most of them are available openly.
So, so that's also the point. And you can go you don't have to use the QR code you can go to.
To tiny. You are welcome. Uh, D.s.
And hyphen Dolan in, uh. And you you can go there.
So so that's that's one of the points. Uh, is there anything unclear about this?
Because this is, uh, so so if someone is still wondering that.
What what's going on? Uh, but but anyway. All right.
Now, uh, and this is the structure. The this is how the also those, uh, articles are structured, uh, that I will follow today.
So we will talk about what I call what we call bibliographic data science.
Then we'll talk. I have two different kinds of. Examples.
There will be more snapshots because I one hour is not enough for me to open up everything.
And that's why you have the articles also. But I want to talk about canon formation in early modern Britain,
a little bit about vernacular ization and what we have called democratisation of reading.
Um, and then I will talk about reception history, uh, in different kinds of ways, uh, for enlightenment studies.
And then I turn to this, uh, new type of place where we are now after enlightenment studies and a lot of the articles that,
uh, we are working on that I'll be talking about through, for example, we do something called translation mining.
Uh, that's something that hasn't yet been published. Uh, but I it's it's so good that I dare there speak about it.
Well, let's go back. Uh, this is also a little bit of a personal journey and, uh, journey, but our, uh, research group.
So let's go back to 2001. Here I am, uh, as a student of intellectual history.
Uh, asking what is intellectual history? If you remember, back in 2001, that was the golden age of of this contextual reading.
Uh, so Skinner is very, very, uh, he's still big, but even bigger in Helsinki especially, uh,
and, uh, so, so a lot of the how to think about intellectual histories is about context.
And what I was started to think already then this is me not being context, uh, computational at all at this stage.
Uh, but what I was thinking, is that, okay, if we really want to do this contextual, don't we want to then really expand the day?
I mean, what we are working with. We had to come here to Oxford Bodleian or uh, just to look at the books.
If we're working on, uh, 18th century, this is the back in the day.
I mean, uh, that's how it was. Another thing that was important for me.
Uh, you see Mandeville and Hume there.
Uh, so, so I'm a simple person coming from near Lapland, where the enlightenment, uh, people thought that, uh, not so bright people live.
So, so I, I, I had a very, uh, when I was reading Mandeville and when I was reading Hume, I got very I mean,
like that people at the time were reading Hume in a way that that is everything but Monday religion.
Uh, and so for that it was just didn't I mean, for me, this kind of reception type of question that how how is, uh, ideas changing?
It really stuck with me for, for a long time, and I haven't got rid of it anyway yet either.
Another thing was that where do you get really the data into early 2000?
And, uh, and this was the time we got one in a, in a trial in Helsinki for two weeks, and I wasn't sure.
Is it going to go away? Uh, so for the two weeks, I sat in the library and I was able to get CD-ROMs and then download things.
There was a limit 50 pages at the time. So I sat through the whole opening time.
I should have brought the CD is I still have a big bunch of them.
Uh, so so that that I just thinking that that that I had this so I can be able to look at the PDFs.
Uh, so so that was the idea. The storage.
I calculated it would have taken about 25 years to to download them at the at the time with that, that modem uh, time, uh, but didn't work.
And of course, I mean, the I mean, if we think about the interfaces, uh,
18th century book, as we all know, uh, survives and if you have it in good conditions,
the CD-ROMs they break and we don't know how the cloud computing and, uh,
supercomputers and so forth will survive in the end, but but, uh, that that's that's where we are.
So then my steps, uh, from being a traditional intellectual historian and then being involved with, uh,
at the great bunch of people from many different backgrounds, uh, our computational history group.
So I really, I mean, that that. When I was doing my PhD, I was getting these ideas that, why don't we do this computationally?
And and different kinds of ideas. Then when I finished my PhD, I started looking for a twin collaboration.
So I called one of my friends who was a professor in medicine, asked that, do you have anybody in your group who would like to work with me?
I have these ideas. That's what we started doing. Then we got, uh, it was two of us at the beginning, and then we had some funded projects.
Some people came in, and then from 2014 onwards, uh, came this phase where intentionally,
uh, took the agency to build a research group, which in the humanities, it's not.
There are people who do it, but it's still a little bit different kind of undertaking,
uh, than working just in the funded projects that we were, we were doing also.
So, so that that's kind of kind of where how things were going.
Now for me, Voltaire Foundation. So my book on Mandeville and Hume, Anatomy of Civil Society, that was published in 2013.
And that's the kind of from there ten years to this point where digital enlightenment studies and we had an article there also.
So, so that this period of time here was very formidable in many ways.
And I feel like that there's a little bit of a kind of kind of overlap with, uh, how, uh, Voltaire Foundation is also, uh, transferring and so forth.
This period when I had finished, uh, this book and was very serious.
Okay. Now I'm going to do I'm going to start this computation. Seriously, people, uh, advising me, my supervisors, that you have a decent career.
Uh, don't do it. Stop. Stop now.
Uh, so.
And, uh, but and then they said that, uh, well, if you are successful, that then that means that we also need to start doing this computationally.
We don't want to do it. Uh, so so that but but anyway, here we are in Helsinki, our group.
Uh, so it's formed so that we have data scientists, uh, and what is and,
and linguists who look at the world and concepts in a different way than intellectual historians.
And then we have a lot of elements of book history, uh, people interested in semantic change,
uh, different kinds of historical processes and so forth.
The what is not always so easy is that to understand that, for example,
if you have data scientists, these are not people who come to fix your computer.
You know, they they don't they are serious scientists, uh,
who need to be thought of in that way so that we are doing work where the identity for everyone is so that you can retain it.
And, uh, we are not like bioinformatics quite yet, so.
So that to think about this, uh, and not, uh, kind of, uh, take advantage of anyone.
So that's, that's the important part. But at the same time, we need to advance together.
Uh, and, and also think that where we're going to get our next funding and and so forth and so forth.
So, so there's a lot of, uh, different kinds of elements and this, this sort of, uh, collaboration that need to be taken, taken really seriously.
Now our aims. This was done. This slide is from year 2017.
Uh, so that what we are trying to do and the reason why I want to show this is that that we've had quite, uh, consistent.
So the strategy that we started already then so understanding public communication and then also looking at the early modern Europe more broadly,
movement of ideas, so clearly saying that metadata work, uh, looking at uh,
genres, looking at intellectual traditions, uh, ancient texts and how they, uh.
Preserved, or how their reception and so forth goes in the early modern period.
Uh, and then looking at uh, also texture use and this coupled with conceptual change.
Very much so the intellectual history underpinnings there. We haven't done nearly enough.
Theoretical work in this line as we would like to uh,
and it's something that also should be encouraged more and more in the enlightenment studies in general.
Uh, but then doing these data releases, uh, doing tools, building tools for others.
Uh, so, so all of this kind of going together was, was what we aimed at.
All right, let me start now. Uh, that from this background.
Go to this, uh, conceptual, uh, and practical level of talking about bibliographic data science.
And this is now the first part where you can look at some of the articles, uh, as well, if you like.
So 2015, uh, it was very clear to us, uh, and we were very enthusiastic that we can map ideas in different kinds of ways,
looking at coming up with this, this network that that is great and bibliographic data and treated properly.
Uh, is something that where we can take these steps and, uh, really look how how, for example,
natural law tradition in some, some sense is, is uh, being, uh, published, uh, across Europe and um,
and take that and combine different elements and move forward and also thinking
about what is the reception of of Mandeville or Hume and this type of elements,
where it was really clear that that this is a good way of, of thinking about it.
And conceptually, uh, we started or I started reading quite a bit about what in bibliography, uh, and in book history, people have been doing.
And there is this debate about potential use of library catalogues, uh, and decades of debate about that.
Of course, the debate about bibliography and science that, uh,
Tencel and Mackenzie and others were engaging it with was a little bit different than what we are now thinking when we are talking about data science.
And then the questions that, in what way are we sure that you can actually use library catalogues to receive?
I mean, to get, uh, decent interpretations of, of, of, uh, how ideas are formed and how they change and,
and what can you do also with respect to book history, only relying on library catalogues?
There's a lot of book historians who think that, uh, the archival, uh, evidence is the only way to, to actually move forward.
We took the other way and, uh, so that you can still debate, uh, and these haven't been resolved in, in any sense.
And it's obvious that, that you need different kinds of approaches.
Of course. Uh, but our argument was that if we take this what we called bibliographic data science approach,
you can actually use in a scientific way, uh, these catalogues.
So for us, uh, the early on, uh, little update from University of Turkey.
It was him. Him and me, we. I worked plenty with him early on.
Uh, and this was kind of like a white paper for us.
Bibliographic data science and the history of the book that was published eventually in 2018.
Um, so if you want to read that, what what where the really the ideas that we had for for this kind of approach,
uh, that is the article, uh, to look at, uh, much of it has to do with the aspects of harmonisation.
So, uh, you if you just take any kind of metadata and think that now I'm going to, uh, get some results that are relevant.
What happens often is that you end up playing with the data. So you are maybe just, um.
Well, explorations can be good. Uh, but our argument was, and still is that that you need to harmonise, uh, data.
There's many different ways of doing that, uh, in order to, uh, get the kind of results that are robust and, and what,
uh, so the thesis, also the analogue traditional, uh, scholars, it mainly it's about the research questions, of course.
So, so what you are asking, uh, is the relevant part, uh, and then what data can you use in order to, to answer those?
In 2018. Uh, it was a little bit of a shift, uh, which was towards, uh, full data, uh, meaning that we have images and we have, uh, the text.
So the echo for us, we came here at the centre, and the idea was very,
very seriously that we are building this kind of knowledge infrastructure for enlightenment studies.
And, and this is also the reason we are able to do, uh, different kinds of things.
These days. Uh, because the combination of the SDC metadata, uh, that includes, for example, uh, having work level and additional level,
uh, sorted, which when you want to do some kind of a text mining becomes a very, very crucial issue.
So the combination between SDC and Agco and then combine of course, with other data sets like VCF, uh,
if we want to look at the influence between French and, uh, English, uh, or British, uh, we need BNF, uh, we need garlic.
Uh, we need information about the printers and publishers.
Uh, and also what we really want is also the newspapers.
So the idea that that if we want to study the public, uh, we need to also, why would we only look at books?
Why don't we also look at the overlap between, uh, newspapers when we are talking about public discourse in the 18th century?
And this was something that not that many people are actually yet even doing that cross catalogue thing.
So, so for us to build from here onwards that we started really seriously in 2018,
moving from the library catalogues to this kind of a knowledge infrastructure.
Uh, that's also something that, uh, has been very, very beneficial for us, uh, throughout and will continue to be so.
Now, one thing that, uh, bibliographic data science combined to this type of work means is in this one article, uh, a two, uh,
anatomy of 18th century collections online, uh, where the idea was that we analyse, uh, what is in echo just based on the STC, which is the best.
Uh, it's not it isn't perfect, but it's the best, uh, information that we have available.
A lot of people want to use the use echo, but, uh, if you don't really know what is in there,
uh, might be a little bit difficult to justify your results.
So just one example. If you want to study pamphlets in an echo and there is this kind of drop, uh, of coverage compared to SDC.
So going from 50, 60% to 30%, uh, I would at least want to be aware of this before I start that kind of work.
And and this is what that, uh, combination enables.
Also, the, uh, like these big gaps, some of them are known.
So people know that there are gaps, but I wouldn't trust what what, for example, the glass company, because they haven't done this.
Uh, so they don't know. So and, and and I think that this kind of it's like preliminary work for,
for any type of, uh, research you want to do using, using this kind of material.
So for us, this is the bibliographic data science, what we want to mean, uh,
when before we start engaging with, with certain, uh, analysis, uh, that we are doing.
Now, another part of the bibliographic data science is the relationship between unstructured and structured data.
So the structured data meaning, for example, metadata from the catalogues, this kind of classic bibliographic data approach.
It's great because it's rather systematic. And then you have unstructured data.
Could be text image whatever, audio collections, anything uh, that can also be analysed in a systematic way.
But it's not I mean, and I don't understand why people don't think about it, that okay,
it might be good to do some analysis, but why would you not think about it at the same time?
Creation of new structured data. So there's a lot of, uh, mark fields, for example, that are missing, uh, from the catalogue information.
But if we can use the unstructured data, for example, to create, uh, genre information or, you know, uh, subject topics, whatever you want to call it,
uh, that kind of a way of working together, uh, in a shared infrastructure, if possible, this then will, uh, make us go much further.
Now, example of this is that, uh, we've started working more and more with the physical aspect and, uh, images, for example, from the echo.
So already I was wondering one part that this comes I was doing it.
By hand. Uh, so I was printing and cutting, uh, head pieces when I was doing my PhD.
And then gluing them to the to the title.
Uh, so sort of, uh, kind of looking at four variants, uh, of, of the different ornaments and.
Okay. Why wouldn't you do this in an automated way and, uh, well, because you didn't have the data.
You couldn't. So, so that this is something, uh, so, for example, my when I was looking for Mandeville, being obsessed, as I always was, that's.
How can people say that Thomson wouldn't be his publisher when all of the everything you look at in the book points exactly that direction.
So and so to me that the idea that that what can we actually do with these, uh, is clear.
There were has, of course, been people who have been looking at printers ornaments, uh, for a long time, usually doing by hand.
But there the idea has been that the, that the woodblock is owned by the, by the printer, and there might be some lending going on.
Uh, but the interest for collecting the information was always just to look for that one tied to Boya or somebody,
uh, famous, famous, uh, printer and, uh, not looking at the variants.
So what we've done, uh, there's also, of course, other people who work with ornaments.
Not saying that, but but there's been discussion whether the echo quality, which is quite poor, uh, is good enough for doing this and well,
we been able to take the 700,000 head pieces from echo and then cluster them in this kind of super and subclasses where we get these variants and,
and some of these are very interesting. So what is the intentionality of producing, uh, these where the eagle, uh,
is looking inwards and some that are looking outward and they are printed in Ireland, the other ones and the other ones in London.
And, and so there's all kinds of it becomes like a big puzzle piece that, that we can then start mapping in different types of ways.
And uh, that combined to better printer, uh, metadata information, publisher information.
There's also, of course, a lot of thought that you can do looking at the type, uh, looking at the fonts, uh, and, and so forth.
But a great way of going here so that we are combining the unstructured and structured.
So, so with the intention that, that we, we get, uh, better data sets and that the project doesn't end here.
It somebody will when we are able to offer it to others, the the work continues.
Uh, and, and we move forward. Also.
Uh, so. So then you can really I mean, that, uh, if you look at these two, two books here,
uh, one should be said set to be published by Roberts and the other one by Thomson.
And that's the one of his, uh, later fable. Uh, and you look at the details, uh, you may start thinking also about the, the author author attribution.
And in this one. Oh, sorry. Uh, the publisher, uh, attribution in this kind of cases.
And, uh, so just an observation. Uh, so they are quite intriguing patterns.
So, so some people say that. Okay, Thomson was a publisher, uh, the younger.
So he didn't have in-house printing. And it was this what, who did all the or the printing?
Uh, but when he dies, uh, some of the uses of the head pieces drops.
Uh, they're not used anymore. So if there were watches, I don't know why that that happened.
And so. So there's a lot of this kind of kind of quite an intriguing.
Questions. We are now working. Uh. I'm not there's no, uh, final, uh, thing that we have said about Thompson,
but but that's something that we are working towards with this head piece of information.
So with bibliographic, uh, data science, what I want to say is that this virtuous cycle of better data, which is a never ending process.
We're talking about better data. So you combine the harmonised bibliographic data, text and image sources, and you come out with, uh,
something that supplements, uh, and helps you use the images then to enrich the bibliographic data.
Uh, there's a loop that move forward. Uh, and then you can also use the text information in many, many different kinds of ways.
And what is really important is that you develop this kind of wit together with the use cases.
So thinking about what kind of research question where in what sense is the data good enough?
Because it never will never be perfect.
We would never be doing an intellectual history if we are just there to let's get this dataset to a perfect condition,
and then you end up annotating everything by hand and so forth.
So good enough and usable and but then you need to be also able to evaluate that.
What does that mean in in different kinds of context.
But this is very much the kind of core of why I want to talk about bibliographic data science.
And okay, I'll make this.
So I publicly say that we are going to finish the cleaning of the our version of the STC and make that available as open data.
We corrected it from 25 to 26. So I give our give us a little break here here.
But but but anyway so so we think that this is uh, a bit of a milestone.
So then after that anyone can can work with it. It will include for example, addition work relationship.
Uh, that will help you a lot if you're working, for example, with the echo, echo data and so forth.
Uh, so hopefully uh, that will also, uh, help, uh, collaborative projects when people work with,
uh, text mining and for example, uh, lamps and, and and so forth.
If we are not done by 26, it will be no fault.
Okay. Uh, then a couple of snapshots. Uh, about what can you do when, when when we are in a situation that that is your data is kind of in a,
in a sense, uh, good enough that we can start working with it.
Can information is something something that we've been very, very interested in,
uh, in, in many different, uh, subprojects or however you want to call them.
Uh, this is one, one project, uh, that we worked with, uh, Mark Hill, who's here?
He was, uh, postdoc, uh, at the time. So we wanted to examine the early morning cannon with the idea that.
What is a cannon here? We just took it so that what is published most often, most frequently.
And for the longest period of time. So. So, uh, means that cookbooks and Shakespeare go on level, uh, as a as a starting point.
And then, uh, we use the addition field information, the work edition, uh, that was really crucial for this.
Uh, and then, uh, publisher and printer information.
And the main interest was really, uh, kind of if we can see an epistemological shift in this kind of kind of information.
Uh, there is an article, uh, before, uh, so, so if you if you want to read it, I'm just going to show a couple very simple,
uh, slides that, that what, what you can, you can do when you are examining this kind of data.
So, uh, here, uh, okay. On the left here, here's, uh, the full, uh, STC most published.
Uh, then if we look at, uh, the new, I mean, first editions, uh, it looks like this.
And then our, uh, uh, cannon.
Cannon data is, is is here, and it kind of breaks down quite nicely in the end.
Uh, when when they are sorted by the first year of publication, this is the number of times they were published.
There's a little bit of way, way in going on, uh, so that later, if you, there's no, uh, data ends at 1800.
So, so that these, these, uh, works can be also included.
And it's not only only earlier. Once. Now.
Uh, so then you can, of course, upset in different ways.
You can start looking at most frequently printed at least during one decade between 1500 and 1800.
So if you look at, uh, what you find, uh, in this way, uh, the works,
there's a lot of things that you would expect to find, maybe some that that wouldn't be so common.
Uh, but that's not the point here. The point is that we we can do this kind of examination, uh, and set it up, uh, when you have a certain interest.
And ours was that that if, uh, we are taking this, this type of, uh, uh, canon, uh, which is justified also by the by the reason that, uh,
the you would the so-called London average of print runs, uh, works and, and that's,
that's if you want to have more books printed, you will get out with the new edition.
So, so that's also a reason why why this this is justified to be doing.
Also, of course. Authors. Uh, publishing after death is important.
Uh, usually, uh, the normal thing is for the non-canonical ones that once you die, they don't print you anymore.
Uh, for whatever reason. Uh, so here also, you find very Voltaire's there, of course.
Uh, and, uh, these are now. Now, remember the the British SDC, uh, data, uh, used here.
So, uh, this was when 21, uh, we were doing.
But then our interest in canon is constant.
Uh, so there's this many more articles. I just want to show one recent thing that we did.
So we took, uh. Um. Well, the article's, uh, title tells you a lot.
Quantifying the presence of ancient Greek and Latin classics in early modern Britain.
And, uh, we won when we were exploring the data.
And it was a lot of work to get the cannon engine cannon right.
Uh, so so that was a lot of work.
But, uh, what we noticed in a data driven way, and what the article is very much about is that one while there was a as a whole,
there's a there's a growing popularity of ancient printing.
But what happens at the same time is the diversity.
Diversity diminishes.
So so the ones that they want, there's many more authors that are printed in the 17th century, but in the 18th century it becomes very concentrated.
So so the so-called martell's effect becomes very, uh, prominent.
So the ones that are printed, most, uh, printed and the other ones, uh, kind of dropped.
So, so we did a lot of, uh, different kinds of, uh, uh, testing, uh, whether statistical testing, whether whether that is right.
If you're interested in that, please, please look at the, uh, article.
Uh. Be good. So then, uh, Verna Colourisation and what we call democratisation of reading.
Uh, these are also now just snapshots. So, uh, this work was done long ago, but I still find it quite interesting.
So if we look at book printing in Latin vernacular in northern Europe.
So there's now many more, uh, data base, uh, for, for this than just the British case.
Uh, what do you notice quickly, for example, from this heritage of the printed book database, is that, uh, book size sizes, uh, shift considerably.
So we go towards much more mobile. So October is the start of the enlightenment.
Uh, not Voltaire. Uh, so so that that, uh, becomes quite clear.
Uh, at the same time, the Latin share that that is dropping, but it's so,
so one thing to note about the Latin share is that, uh, it's not a synchronised move.
Uh, so very looking at the local local differences, different cities have different profiles.
Uh, so we can't say that there was a uniform move, uh, or something like that.
Yet from here, uh, you could start. I mean, not not saying.
I mean, the the Latin, uh, part.
We looked quite carefully, uh, the different profiles, uh, but from the, the document size, uh, what do you want to make out of that?
Um, that's another other question. And how how red or thick?
You want to see the connection? That's that's another issue.
But, uh, especially the drop of the Latin share, uh, might suggest that the reader community is at least getting wider.
Uh, so also, uh, those who didn't go to college, uh, are starting to read, uh, and which is,
uh, important part and part of, uh, this movement and then combined to this, this is a,
uh, article led by, uh, doctoral researcher Eero de Honan, uh, who's very interested about the kind of like,
uh, looking at book history from the economic perspective as well.
So, uh, probably everybody knows this, uh, idea that there was a monopoly in London and the book prices were kept artificially high.
Uh, and then when a lot of people have been interested about this copyright ruling, uh, that that there is this public domain that comes,
uh, and then that the, the, the prices would have gone down considerably when we looked at this from the perspective of price constraint.
Uh, the obvious conclusion was that that didn't have such an effect.
So the, the reading still was, uh, mainly for the, for the, well, I mean, well, not reading, but but book ownership was, was for the wealthy.
Uh, and then more emphasis could and should be put on this kind of different kinds of circulation, uh, resale or lending practices and so forth.
But, uh, a good way, in my opinion, to show how, uh, this bibliographic data, uh,
science perspective can bring a new aspect to something that has been debated over decades.
The copyright question is, uh, is a huge issue in, in, in book history.
And, uh. This is about bringing a new perspective.
There was, uh, we calculated, uh, probability for the whole SDC for the book prices.
And there was, uh, quite a lot of work gone into that.
But if you're interested, please look at that, too. Now reception history.
Which is also something that, uh, in our workshop now, over the two days, uh, we've talked quite a bit about texture use, uh, and such things.
This is also something that we in Helsinki we've been working quite a bit with.
Uh, so we have run this blast bioinformatics, uh,
software and all of the TCP 18th century collections online and also then the British Library newspapers, Burnie and Nichols collection.
And we have about a half a billion, uh, text reuse pairs.
And, uh, what do you I mean, why that bibliographic data science for doing this is so important is that
that we also need to have the SDC frame and the possibility of getting rid of,
uh, multiple editions, uh, in certain cases if we want to really analyse that, that what is happening here.
But, uh, so we doing different kinds of reception studies, uh,
patterns of borrowing impact in some sense and, and also what we might call discourses through the,
this kind of dissemination of texts and what again, that knowledge uh, in uh graph uh,
that we want to see uh, so that we go beyond one single digital archive, we have plenty of them.
And then we are looking to step from some kind of simple textual overlaps to mapping ideas so that we are we are able to classify and understand,
uh, what the texture uses, for example, are or what uh, Glenn call today, uh, intellectually relevant, uh,
texture uses that are more interesting than just mere Bible being, uh, quoted many times.
So, uh, for us, uh, just to again, I want to go back to, uh, back in time, 2019 was still relevant.
Uh, the ideas that we had then, uh, these were the basic questions that we came with, uh, when looking at the tax free use.
So if we take Mandeville, uh, what kind of patterns of borrowing, uh, did he have or what kind of habits of reusing his own works did Mandeville have?
Often with texture use, we tend to think that let's get rid of the author's own works.
So because they are not interesting, in some cases they are very interesting.
And then, of course, who quoted Mandeville and why?
So there's quite a lot of, uh, if we are a little bit clever with like the simple data, we are able to do quite interesting things.
And it's not always running for the next, uh, biggest model or something like that, but using what we have.
And that's where, uh, humanities people could be much more effective and, uh, thoughtful in many sense.
This is again, uh, the, uh, image from 2017.
But I really like it because a lot of things came out because of this.
So what we called anatomy of the impact.
Mandeville. Fable of the beast in 18th century. So what we have here is the beginning of the book.
And here's the end of the book. These, uh, count of how many times different chapters, uh, reused.
And then the colour tells you, uh, when is the reuse happening?
So what we notice here, uh, that the beginning of the book, when there is, uh, uh, Mandeville makes a lot of noise.
So this part where, uh, private vices, public benefits is said, uh, it's used in 20s and 30s, of course, people complaining quite a bit about it.
Uh, but it's something that, that then, uh, vanishes a little bit after that.
And then there's an interesting shift.
Uh, so the part, the chapter where he discusses luxury and pride, 1760s, there are many more times that this is this part is quoted.
And then, of course, we can see what is not quoted at all. So Mandeville was a vegetarian.
So he wrote a chapter called Why Man's Craving flesh for food is unnatural.
Nobody is quoting it in the 18th century. But, but but uh, so this kind of, I mean, like that we can do this kind of anatomy of a book.
I mean, of course, this is, uh, like, like a visual way of looking at how, uh,
different parts of a book are reused, but it's so useful and especially when it's scalable.
So you can do it, uh, for basically for any, any 18th century books, because we have the, The Echo collection.
And this kind of shift especially to me is extremely interesting that that and and also that the
question that why quantity uh sometimes is matters and we can see some kind of shift then of course,
I mean, if you want to go uh, we developed based on, on mainly this idea here, uh, we developed this, uh, reception reader.
Uh, so if you go w w w reception reader accom uh, you can use this interface.
We are very grateful to Gail, uh, that they let us, uh, publish this openly so anybody can use it.
You don't have to have a subscription. Uh, and it works so that.
Okay. And here is the beginning of the book, and here's the end of the book.
And there is a timeline, all of these, uh, dots here, uh, reuse cases.
So you hover over one, uh, here, you see, when you are hovering over it's interactive, you see which book are we talking about?
And then you click on the on the dot and it opens up the pages.
So here on the left is the original. And here on the right is the blue.
It's uh, uh, book that is quoting this, uh, part of wonderful.
And, and it's highlighted here again, I mean, very easy, intuitive, uh,
and meant for intellectual historians who don't want to do necessarily anything statistical.
But this is what people used to do. I mean, that you are looking I mean that, uh, do I find the same things?
Uh, and here you can have an interface that does it, does it very easily.
So David Rossen, uh, from our group is the one, one the front end guys, so to say, uh,
who, who very quickly, when he came with us, uh, we were able to, to put this together.
Now, uh, I want to show a couple of things that you could do with text reviews that you maybe wouldn't think, uh, very ugly graph.
But what you have in here, in this matrix, uh, is, uh, Manuel's, uh, all of his own books, uh, and, and textual overlaps between them.
And so one thing that Mandeville is very well known for is he recycles passages from his own works,
and then he cannot be without quoting Bayle or taking something from Bayle, not quoting, but but just taking material.
This happens in almost all of the works except one, which is public use.
Uh, if you look at the reason why public use is attributed to Mandeville, it becomes quite questionable.
That is he actually the author? Uh, so we there's there's an article.
Uh, that's in the digital enlightenment studies. Uh, we we wrote about Mandeville and where we talk about it.
Uh, we are not saying that this is now concrete evidence that we have done everything to show, but this is evidence to at least think about it.
That that. The way that oath.
I mean, people frequently quote, if people want to talk about prostitution and then they, they quote, say that public statues is by Mandeville.
Uh, but the reason why is this by Mandeville is that everybody thought that it is by Mandeville.
So, so that the evidence is super slim.
Also, if you look at the publisher, uh, information, uh, about it, um, there are reasons that you might want to doubt it.
Also, uh, one thing. So. So we mean, the way that we did this addition work and then collection level was based on, uh, titles from the HTC first,
but then when we have this text reuse information, what we did, we computed a coverage metric from echo and bought TCP.
And then it's a directed one. So which way is the I mean now we are talking about works of the same, same same book.
Uh, or might be a collection of, of something else.
But the question that how much overlap there is between the same, uh,
additions we are involved in Hume's history of England, uh, for Clarendon edition.
So we need this kind of, uh, overlap.
Uh, question for, for many, many different reasons, but what do you get here is a possibility based on the text we use.
So if we have a work and edition one in addition to have, there's 90% of material that is the same going this way and 75% going that way.
Okay. You can make, uh, different kinds of, uh, assumptions from there, but they are still probably the same addition.
But when you have a situation where 90% of the text goes to this edition here, but only 30%, uh, is is kind of covered like this,
there's a good reason to start thinking that maybe we're talking about collection level instead of a work level,
but for many, many different kinds of reasons. Uh, extremely important.
And one good way of thinking that how do we want to use, uh, this, this type of, uh,
text reuse material that might be seemed trivial to someone, but it most definitely is not.
So. Yeah. So just to string matching, uh, the newspapers as an intellectual platform.
Uh, that's something that I would really encourage people to think about.
Uh, and also this kind of way of using this for critical editions.
Uh, we are building, uh, scholarly edition tool that we're and we talked about that, that today, uh, where, where this is,
uh, coming to use and also we want to make this available for others so that, that, uh, making, uh, kind of.
Enlightenment studies, uh, move forward in this sense as well.
Okay, I for Enlightenment studies. Uh, this is my last, last part.
Um, so this now, uh, for me is is a quite, uh, important, uh, slide.
Uh, so we're trying to conceptualise, uh, between text reuse and something we call semantic similarity.
And this has much to do with the what we are able to do with, uh.
Embeddings. Uh, so so also if you want to say that.
Okay, what can we do with AI and large language models.
This has much to do with it. So if we think about parallel text uh, and uh, what is kind of overlapping or has to uh.
What connects them in a way. So one way is to think about this kind of lexical text reuse.
Right. That, uh, includes artefacts.
Uh, so there's a lot of, uh, if you think about newspapers and books, uh, the, uh, advertisements for books.
So basically, uh, imprints, uh, the one of the most repeat repeat that elements.
There's also OCR errors. You could study those if you'd like.
Uh, then the the reprints. Uh, that's that's an interesting question for us at least.
Uh, you have quotations, uh, near verbatim, uh, verbatim.
And then you have also extensive, uh, reuses and secondary quotes and that kind of things.
But then this continuum, uh, here, uh, when you are thinking about semantic similarity, this is extremely important.
Illusions. You can study them, but it's on. Right. Because we are not doing it at the moment.
You can think about paraphrasing, uh, and also what is really important for us is translations full, partial, combined and so forth.
And then also something that we call meaning matching.
Uh, and this is, uh, really, really, uh, interesting, but at the same time maybe difficult so that,
that when what is the meaning match and when is there like a intentionality between
someone commenting and when are we just talking about topic or similarity?
But this is, uh, something that it's for the US, the intellectual history and, and uh,
humanists to sort out the methods are there, but we need to be able to define what, what we really want to do.
So, uh. This is our sort of aim to, to make sense of,
of what we are talking about when we are talking about tax free use and when we are talking about semantic similarity and meaning.
So, uh, what we'd like to do. And there might be other ways also to think about it.
So. So this kind of taxonomy of meaning or how do you want to do it?
But the very important point is here when we are thinking about the meaning matches, uh,
the multi uh lingual uh language models, uh, uh, transformer models, they are able to do it across languages.
So the language barrier is not a problem anymore.
And this is the what what probably going to change quite a quite a bit of research, uh, when we go towards the future.
Here's an example of what is the lexical overlap. So quote there might be this kind of OCR noise as you see here.
And and here is an example of a semantic similarity.
So we have a small interface where you can type in uh about women humans rights and equal to men.
And then you can hit uh, the based on what it classifies as, uh, something that is closest to that, that that's an example of, of.
How how how that that works. Now, maybe the most interesting thing about from this where we are now, uh, concerns this translation mining.
So we created embeddings. Uh, so we turned the 200,000 books from echo into this, uh, embeddings, uh, that are.
So every single book is chunked.
Uh, so, so that they have, uh, have their own own, um.
Uh, vector. And, and then we turned to 300,000 books that we get from garlic into similar ones.
And then they are mapped. Uh, and, and there are different kinds of filtering steps.
Maybe I don't talk about that, uh, now, but, uh, what we get is this type of, uh,
as an output, uh, where you have the English book here, the beginning and the end.
And here's the French book, uh, beginning and the end.
Each single one book compared to each other. And then different kinds of filtering.
What is relevant, what is not. Uh, and you can start there are these diagonals that we are looking for then, uh, to recognise that.
What is the translation? Here's a very interesting case.
We have a book that has a British introduction. No translation.
Then you have a part that is translated this here in red.
Then you have some original text the British, uh, have added here text.
Then you have another translation part, and then you have another, uh, from a third, another book.
There's more translation here at the end.
Uh, so you can do I mean, all we have done this to every single one book that, that there are in these collections.
And if you think about it, I mean that that.
Cases that that you could start developing from there, uh, different kinds of intentionality in translation and also thinking about that.
Uh, we are not interested only in the full translation, but, uh, different kinds of ways of, of of, of doing the translation.
That's, that's quite interesting.
And of course, I mean, look here, uh, you the because there are these green that go to lower our partner, uh, Philip Ginter from Turku.
He is very angry because he wanted all of the nicely on green here on the top.
Uh, so, so that that we could say that it works perfectly.
Uh, but, I mean, so, so that these different kinds of evaluation methods are just as important as doing the,
uh, development of the methods that that take us towards this direction.
And then also, of course, there's a lot of manual annotations.
Uh, just check that, that what is actually the translation.
So the between commentary on Bible obviously isn't a translation most of the times.
Uh, but uh, just as something on the same topic.
So, so these kind of things you need to sort out, it's a probabilistic approach.
So we're not claiming that is 100%. But here if you see a clear semantic and intellectual similarity check, it's a translation.
And then for us, kind of what is important is also developing a kind of taxonomy of translations.
So, so, uh, going a little towards the Linnaean direction that we are not saying that these are set in stone,
but we want to kind of organise knowledge and think about the world in a way that we we are doing different and we have the
we are brave enough to to say that there are some kind of taxonomies that organise the knowledge in this humanities world.
And even when, uh, this class and that class are individual and they can be studied,
you know, as objects of their own, but this may be some kind of similarity between objects.
So, so that's also something, something that we've been developing.
And then we get to the meaning of meaning. So, Rousseau.
Man is born free and everywhere. He's in chains as it's translated into English.
Uh, so what is interesting is the reinterpret interpretation used in misuse in different contexts.
This is on the plus side. We did this together with Ana.
Uh, so, like, really that the interest in.
I don't want to say that everything is reuse or the meaning is reuse or.
But but oftentimes the meaning comes from that.
Uh, and so first it's uh, taken as a moderately in criticism or criticism towards Mandeville.
So there's vices uh, commercial society will lead us French Revolution suggestion to take arms against monarchy.
Uh, then it's a claim against slavery. Some people said that.
Well, clearly it's, uh, uh, you can use it in the pro-slavery way as well.
Uh, and then, of course, uh, him being criticised, that principle doesn't apply to women.
But so what we can do algorithmically. So we start separating engagement.
Now, now here the the meaning, uh, comes through the engagement.
So we see that that okay, here is not, uh, it's not a text you use, but, um, similar meaning.
And there's an engagement to Rousseau and there's then a context where it's understood and those contexts are shifting.
So a little bit about the question where we started that what is intellectual history?
But why aren't we. In intellectual history.
Interested in theory anymore? I mean, we really should be.
And there's such good opportunities here to start thinking about this also for the people who don't want to be computational.
And that that I mean, it's it's a it's a silly thing that if we think that everybody needs to be computational.
Of course not. But I mean that these, these tools and opportunities need to be.
You know, they need to embed what we are doing. Uh, because otherwise we are just looking at Mandeville and Hume and, you know, so.
So that we can still do it. But but but let's move forward.
Okay, I'll skip that. All right.
Oh, so let me just say.
Conclusions. So also with respect that if I'm thinking that use of computation in kind of historical humanities,
it was for a long, long time and still I mean summed up by its mixed methods enterprise.
Right. So you use algorithms to spot patterns, and then you go to the classic close reading to make the interpretation.
So that that's that's quite often what people have been doing.
But I don't think that the next step forward. It doesn't have to be another algorithmic novelty.
I think with this, this kind of stuff, we have quite a bit already.
So the tools are here, but then the theoretical growth inside that that will take us forward.
So instead of kind of chasing for the next quick mix, uh, genuinely scientific mindset.
That we are really blending the domain knowledge and rigorous data analysis,
and then this kind of large scale infrastructure gains would be much higher than than what we have here, for example.
So then the ad hoc fixes, remember this kind of one off topic models that people wanted to do maybe still,
but they postpone the kind of bigger ambition. And, uh, so really sustained collaboration between historians and, uh,
data science and, and so forth that I think a lot of people are pushing towards that.
So then the AI comes to serve us, and we are not just some kind of mascots in other people's projects.
So also maybe this kind of computational hermeneutics that should push the machine towards the.
Or beyond the preliminary pattern hunting.
Uh, or maybe the modelling. Modelling history.
What would this mean? And what do we want to do? So. So this should, uh.
Lead us to reshape. Uh, also the interpretive act.
And, uh, so the interpretation is no longer something when the computer is completely put aside and then,
okay, I'll think about it and make my own biased, uh, view of of things.
So iteratively and, uh, kind of in concert going, going forward.
And I really don't think that this will eclipse or the use of AI that that would
eclipse anything from take anything away from us in a in a more substantial sense.
And it will give us, uh, we sharpen the understanding of historical change and still rely on the our interpretive judgement in a,
in a much bigger, bigger, grander scale. Uh, as part of, uh, this kind of integrated interdisciplinarity.
And of course, I mean, we will change, uh, as, as we move along.
But that's how the world works. The got stop here.