- Hi David!
- Hi Simon!

This is a brief introduction for students,
or potential students, to our course:

Foundations and Applications of
Humanities Analytics.

And I think the opening question 
might be:

why is a philosopher of science
- you're a philosopher of science -

why is a philosopher of science
leading this course?

That's a great question, Simon,
and I think it really comes down to

by taking an epistemological approach,

also you might call it 
an epistemology first approach,

to humanities analytics,
sometimes called digital humanities,

or cultural analytics, what we're doing
something very different from other

introductions to this field,
that might be offered to a PhD student

or a scholar in the humanities,
which is our core audience.

And what we're doing there, is rather than
saying:

"here are some tools, 
go ahead and use them

to do quantitative work
in the digital humanities"

we're starting with the question:

"what is the epistemology of
this new science?" Right?

What is the process by which this new way
of thinking about human cultural output

allows us to form justified beliefs and,
possibly, even knowledge, if you think

there's such a thing as knowledge.
I'm personally a little skeptical.

So that's really where I think a
philosophy of science,

and a philosophical approach, more
broadly, to humanities analytics

really has some value. So that would be
my answer to that question.

Yeah, I mean this is interesting,
David. The idea of beginning first with:

"How are we going to go about knowing
what's the nature of the subject itself?"

That's something I think that's been
difficult for those of us coming from the

technical side, scientific side, 
coming in.

Because the criteria for what it means to
get something right is very different.

The kinds of things we're interested in
knowing are very different

in the sciences versus the humanities,

and part of this work,
to me, has always been:

How are we getting things right for both
sides of the aisle? As it were.

Yes, I think that's right. And I
think that what's important to note

is this is a very new field of inquiry,
so in that case it's to be putting

together this kind of course, and to be
trying to address this.

Not by "Here's how to plug in your 
corpus into an LDA topic model",

but rather to start a little earlier.

We will get to things like that.
But to start a little earlier by saying

Here is this new science
what are its foundations?

What are its goals?
What are its interests?

I think is a really cool way to start.

And I say "new science", it's
also a new approach to the humanities.

So what you say about hitting both sides
of that aisle is exactly right.

Again, our focus is really on an audience
of humanities scholars

and people whose interest is,
first and foremost, in some area of

cultural output by human beings,
and taking it from there to try and see

how we can use some quantitative and
computational tools to make new progress

in these areas and gain new insights.

One of the things that's always struck me
is that, in the end, I've never done

a project in cultural analytics that
doesn't involve a collaboration

between people whose PhDs were 
in technicals, M fields,

and people who write monographs 
for a living, right?

One of the things that I remember,
the first time I pitched this to

an audience of historians, and work we
had done for an audience of historians,

someone in the back said
"How many books of history do we have

to throw out, because of what
you've just done?"

And, what he was really after, of course,
was: what kind of knowledge is this?

What have you done? How does this bear
on a practice that is

much, much older, in fact,
than any of the empirical sciences.

I think that's right, and I think it
highlights what I see as

a really important goal of this course,
perhaps, maybe, THE goal of this course

for students, which is: we want to use
this course to train humanities scholars

to get to a point 
where they can be collaborators

within digital humanities projects.

So I think you're exactly right, that sort
of humanities analytics,

digital humanities project works best
when you have a team that includes

a humanist and someone from a more
computational and quantitative background.

At least those two. And then often more,
even three or four, authors is really

a really nice number, often.

And that's really what we're trying to do
here. Is trying to bring people who come

from more traditional humanities
backgrounds, to that place where they

can approach someone, who maybe
works in a quantitative science department

or a computer science department, and say:

"I've got a really cool question,
that I think can be answered

by computational analytics techniques. 
Let's work together.

But let's not have it be totally siloed
off, where I'm just doing the humanism

and you're just doing the computation."
But let's actually work together and find

a nice meaty part of that Venn diagram
that intersects.

So that we can really bring something new
to the table, rather than just two people

doing two things there in space then
mashing that together into a paper.

Well this is one thing that comes up now.
This is now something you can actually do

as scholars. We've always had people in
the humanities doing quantitative analyses.

I was just reading that Alistair McKinnon,
the philosopher in the '60s,

did a stylometric study of 
Kierkegaard's Pseudonyms-

- oh wow!
- which is quite fun, so I'm not quite

sure what to make of it -
he takes the ratio of logarithms,

which I'm unhappy about.

But it seems like these team efforts are
increasingly part of the culture

in the humanities. We have coming up,
as part of this course, guest lecturers.

All of them are going to be talking about
how this work gets into the scholarship

in the humanities.
Another question is: how can we do this

work and not have it be a one-off, kind of
fun thing you did on the side, but how can

this actually help someone build a
scholarly profile - build a body of work

that will last a long time.

That's a really good point, and I think
I'm glad that you mentioned the guest

lecturers in this course, because if
someone's watching this introductory

video and thinking I'm just going to have
to listen to these two for 10 - 14 weeks...

no, that's not the case! We've got a great
lineup of guest lecturers, and just three

to highlight now are: Lauren Klein,
Richard Jean So, and Julia Lefkowitz.

Each of which has been given the brief
to present something that's meant to be a

little bit inspiring. We're going to be
talking a lot about foundations, a little

bit of applications, as well, giving
students the nuts and bolts of what we

see as a good humanities analytics
scholarship. But that's different from

seeing a finished project, and while we
will present some finished projects,

I think it's really great to bring
someone else in,

in Julia's case, for instance - here's
someone who wrote a PhD thesis quite

recently, in a digital humanities area,
and can really walk people through how

that project works, and how she actually
got from a humanities background to

actually doing a fully-fledged scholarly
project in digital humanities.

Similarly, with Lauren Klein's work, I
think we get a really great sense of how

her really deep interest in core humanities
topics - like power and how power has

worked through history and American
history - led to this sort of quantitative

project that yields some really 
interesting insights about who was leading

and who was following in the language of
abolitionist papers. And then, finally, in

reading Richard Jean So's presentation,
I think there's just such innovation in

the way he finds sources, to think about
storytellers are interacting with COVID-19

and this area of pandemic, that we are
obviously still in. So I think

between those three projects, and there
are other guest lecturers as well, but

between those three projects, it's just
a really nice sense of what people are

going to get out of this course, beyond
just the nuts and bolts. Sort of seeing

how they can see their own research
through the same prism that these

guest lecturers have.

Yeah, I think one of the key things that
we've been doing here is - there's plenty

of really fun theory one can do about what
it would mean to investigate an aesthetic

experience, an historical event, 
quantitatively. We're really focused on

getting people up and running in their own
projects. Our goal here is things that

people can immediately start applying.
Maybe they're not beginning a PhD thesis

on something they've learned in lecture 2,
but getting people to the place where they

can start doing this. They don't have to
buy our particular epistemology in order

to get going. One thing - one of the big
features of this course, that we've been

funded to do, which is particularly
exciting, is run - in parallel with the

lectures each week - is run an online
discussion forum, where people will be

able to exchange ideas, talk about things.

We'll have a TA, Zachary Donovan, who'll
be working on assignments that people have,

joint writing projects, ways for which
people can, not only, try to solidify the

understanding they have from the lecture,
but also, hopefully, build collaborations.

Build networks and relationships with
other people who are following along.

Yeah - as much as is possible within
an online course format, we do see

one of our metagoals for this course
is to build the next generation of

humanities analytics scholars. And a big
part of that is creating connections

because for everything we can teach,
and everything we can lecture about,

there's no substitute for the sort of
organic connection that happens when

two scholars come together and realize
that there's a common interest,

and a common interest in investigating
that interest. And that would the greatest

if we can facilitate some of that. I did
want to go back to one thing you said

about "we don't need you watch lecture 2
here and go off and write a PhD thesis..."

I think one of the nice things about the
humanities analytics paradigm is because

the unit of a "project" tends to be a
paper, rather than necessarily a full

book, it is possible for someone to
come away from this course, have an idea,

build it up a little bit more, and develop
it in a reasonable amount of time that

isn't going to totally sidetrack perhaps
the more traditional humanities

scholarship. So if you're listening to
this and thinking:

"well, I have the book that I know I want
to write"

and it's ultimately a qualitative project
in my field of expertise, that isn't

meant to keep you from doing that. It's
augmenting your scholarship. It's not

taking anything away from your other
scholarship, because I think we're both

people who love the humanities, and
don't want to see what we're doing as

in any way in competition for resources
or research time with more traditional

work in humanities. It can be, and often
is, excellent, so we don't want to see

ourselves as in competition there at all.

Yeah, I think this is a great thing to
bring up.

There's different ways this kind of work
can get into scholarship.

So, our guest lecturers, by and large,
are talking about cultural analytics

projects from start to finish. In Lauren
Klein's case, and Richard Jean So's case,

these wind up being papers in some of
the top humanities journals, certainly.

There's lots of venues for people to
publish those works,

the Journal of Culture Analytics, for
example, but at the same time, I think

this course could serve as a way for
people to supplement the research they're

doing. This could be some tools that would
enter in a single chapter of

a larger monograph, a larger dissertation.

So, this can be a supplement to work
that one's already doing.

But this can also be a jumping off place
to do original work from start to finish.

A piece of work where the qualitative 
and the quantitative are

inextricably tied together.

That's a really point. I do want to
emphasize though, because we're going

a lot down this path of thinking about the
relevance of our course for its core

audience, which is a sort of PhD student
in the humanities - that's how we've

designed the course - but that isn't to the
exclusion of other people getting a lot

out of this course. We've always said that
while we do have this core audience,

it's also the case that we would love for
people, whether it's more senior scholars,

or maybe an undergraduate who's just
very interested, or someone who works

as a librarian, or something like that, to
get more into humanities analytics

foundations and techniques. Certainly
nothing we've said here should be to the

exclusion of someone like that getting
as much as possible out of this course.

And maybe it is just doing more
scholarship, or maybe it's just having

a broader understanding of this world
and how it works.

Or on the undergraduate side, maybe
thinking differently about the kind of

academic trajectory they'd like to pursue
going forward.

I think all of that is possible, and the
course is designed to have that flexibility

as well. I think the assignments are
broadly written enough, and broadly

pitched enough, that anyone with an
interest in humanities analytics

can get a lot out of this course.
I would hope.

I mean, so yeah, our mandate from the
National Endowment for Humanities

is broad. There's no particular career
stage that you need to be at.

There's no particular career stage we
expect you be at.

For many people the unit of work is
the journal paper, certainly in the sciences.

For many people it's the monograph.

But also for many people it's the website.

It's the undergraduate class.

Certainly, humanities analytics is
something that's showing up in

the liberal arts colleges, just as much
as the research universities.

What is our mandate? Our mandate is
to enable people, to empower people,

to do great scholarship, of any form,
at the cutting edge -

of whatever is actually going on when
we open these books, but also open

our laptops at the same time.