Anticipating the Turn


Breton’s apartment in Paris, filled with objects he and Levi-Strauss bought while exiled in New York during the war.

As with any intellectual history, there is more to “the turn toward performance” than meets the eye: there is considerable buildup across a broad intellectual front, including the introduction of existentialism into the American academy and public culture. (E.g., William Barrett’s The Irrational Man [1958] — see note below). A consideration of these broader trends would reveal that the acceptance of work by Martin Heidegger, Jean-Paul Sartre, and Albert Camus following the second World War was anticipated by work in American philosophy, such as John Dewey’s Art as Experience (1934) and Kenneth Burke’s Philosophy of Literary Form (1941). Some of the effort to discern a particular American culture was in response to the rise of rich international connections (which manifested in politics as a concern over communism), many of which were brought about by displaced intellectuals who came to the U.S. in the thirties and forties. Some of them stayed and the result was that American intellectuals interested in work by Roman Jakobson found him referring to work by Vladimir Propp and Mikhail Bakhtin, and so American scholars found themselves confronted by an entire school of literary theory, now known as Russian formalism, which interacted somewhat with their emerging interest in structuralism as it had been developed in France by Lévi-Strauss, Piaget, Lacan, and others. (And all of this ignores the many contributions of the Frankfurt School during this time.)

Burke, Kenneth. 1973/1941. Literature as Equipment for Living. The Philosophy of Literary Form. Berkeley and Los Angeles: University of California Press. Pp. 293-304.

Jakobson Roman. 1960. Closing Statement: Linguistics and Poetics. In Style in Language, 350-377. Ed. Thomas Sebeok. MIT Press.

Lord, Albert. 1960. The Singer of Tales. Harvard University Press. (The link below will take you to an online version of book hosted by Harvard University.)

Note: If you have never had the chance to read Barrett’s The Irrational Man, I highly recommend it. A survey of its chapter titles should prove reason enough: from “The Encounter with Nothingness” and “The Testimony of Modern Art” to “The Place of the Furies,” the book was the gateway to existentialism, and thus also phenomenology, for many.

Hannah Arendt holding court at the New School for Social Research.

Python’s `google` Module

So, like me, you become interested in the possibility of executing Google searches from within a Python script, and, like me, you installed the google module — which some have noted is no longer developed by Google itself but by a third party — and got an import error, here is what happened: yes, you did install it as google:

pip install google

but you do not call it google because that will lead to an ImportError. Instead, the name of the module is googlesearch, so what you want to do is this:

from googlesearch import search

Now it works.

Hat tip to shylajhaa sathyaram in his comment on GeeksforGeeks.

Labels

“One popular misconception [about machine learning] is that people think they have enough data when they don’t. When people say machine learning, a very large segment of predictions are based on existing data. And in order for that to work, you generally have to have a big labeled set of data,” says Hillary Green-Lerman of Codecademy.

Emphasis on labeled.

Later:

“People often don’t realize how much of machine learning is getting data into a format so that you can feed it into an algorithm. The algorithms are actually usually available pre-baked,” Hillary said. “In a lot of ways, you need to know how to pick the best linear regression for your data, but you don’t really need to know the intricacies of how it’s programmed. You do need to work the data into a format where each row is a data point, the kind of thing you’d want to pick.

Difficulties with PIP

As I have noted before, the foundation for my work in Python is built on first installing the Xcode Command Line tools, then install MacPorts, then installing (using MacPorts) Python and PIP. Everything I then install within my Python setup, which is pretty much everything else, is done using PIP, so when I kept getting the error below after finally acquiescing to macOS’s demands to upgrade to High Sierra, I was more than a little concerned:

ImportError: No module named 'packaging'

See below for the complete traceback.1

I tried install setuptools using MacPorts, as well as uninstalling PIP. I eventually even uninstalled both Python and PIP and restarted my machine. No joy.

Joy came with this SO thread which suggested I try:

wget https://bootstrap.pypa.io/get-pip.py
sudo python get-pip.py

Everything seems to be in working order now.

Traceback (most recent call last):
  File "/opt/local/bin/pip", line 6, in <module>
    from pkg_resources import load_entry_point
  File "/Users/john/Library/Python/3.4/lib/python/site-packages/pkg_resources/__init__.py", line 70, in <module>
    import packaging.version
ImportError: No module named 'packaging'
~ % sudo pip search jupyter
Traceback (most recent call last):
  File "/opt/local/bin/pip", line 6, in <module>
    from pkg_resources import load_entry_point
  File "/Users/john/Library/Python/3.4/lib/python/site-packages/pkg_resources/__init__.py", line 70, in <module>
    import packaging.version
ImportError: No module named 'packaging'
~ % sudo pip install setuptools
Traceback (most recent call last):
  File "/opt/local/bin/pip", line 6, in <module>
    from pkg_resources import load_entry_point
  File "/Users/john/Library/Python/3.4/lib/python/site-packages/pkg_resources/__init__.py", line 70, in <module>
    import packaging.version
ImportError: No module named 'packaging'

  1. For those interested, the complete traceback looked like this: 

Classifiers

When considering a classifier, effectiveness can be considered in terms of accuracy as well as precision and recall. (Precision and recall seem to mirror “senstivity/specificity”, unless I am misunderstanding those terms.)

Bookends, at last

In September 2016, frustrated with data that had gone missing in a transition between versions of the reference manager I had been using and liked very much, I listed the following specifications for what I wanted in such an application:

  • drag and drop input with autocompletion of data fields with as few clicks as possible;
  • storage of documents in human-recognizable containers: articles, books, etc. with names that look like in-text citation: e.g., Glassie_1982.pdf;
  • ability to scan PDF for highlights and notes and to print those notes and highlights separately;
  • ability to indicate if physical copy is present — or if physical copy is only copy — and its location — the ability to check out a physical copy would be useful;
  • ability to handle epubs gracefully — being able to read and mark them up within the app would be nice.

I am relieved to note that Bookends has much of this. For most items with a DOI, it can fairly quickly grab all the needed metadata — there really is no reason that at this moment in time anyone needs to spend time filling in those fields themselves. (I should note that occasionally Bookends either confuses the order of author’s names in BibTex files or, perhaps, that information is recorded properly in BibTex.)

While I do wish that Bookends would give me the option of replacing spaces with underscores automagically, when it offers to rename files it does so sensibly and in a human-readable form and in a location of my choosing.

Bookends’ tagging system remains opaque to me, but I’ve compensated by creating groups that do much of the work of tags. I’ll live with it.

What We Talk about When We Talk about Stories

Rejected for a special issue of the Journal of Cultural Analytics, but, still, I think, an interesting project and one I will continue to pursue. If anyone else is interested, this is part of a larger project I have in mind and I am open to there being a working group.

Current efforts to treat narrative computationally tend to focus on either the very small or the very large. Studies of small texts, some only indifferently narrative in nature, have been the focus for those interested in social media, networks, and natural language technologies, which are largely dominated by the fields of information and computer sciences. Studies of large texts, so large that they contain many kinds of modalities with narrative the dominant, have largely been the purview of the field we now tend to call the digial humanities, dominated by the fields of literary studies, classics, and history.

The current work proposes to examine the texts that fall in the middle: larger than a few dozen words, but smaller than tens, or hundreds, of thousands of words. These are the texts that have historically been the purview of two fields that themselves line either side of the divide between the humanities and the human sciences, folklore studies and anthropology (respectively).

The paper profiles the knot of issues that keep these texts out of our scholarly-scientific systems. The most significant issue is the matter of “visibility”, of accessibility, of these texts as texts and thus also as data: largely oral by nature, most folk or traditional narratives (must) have been the product of a transcription process that cannot guarantee the same kind of textuality of a “born literary” text. (The borrowing of the notion of natality is somewhat purposeful here, since we often distinguish between texts that have been, sometimes laboriously, digitized and those that were “born digital.”) As scholarly fictions, if you will, they are largely embedded within the texts that treat them, only occasionally available in collections. With limited availability, and traditionally outside the realm of the fields that currently dominate the digital humanities, folk/traditional/oral narratives are not yet a part of the larger project to model narrative nor of efforts to consider the “shape of stories.”

This accessibility gap has overlooked both human and textual populations: most of the world’s verbal narratives are in fact oral in nature and millions upon millions are produced everyday by millions and millions of people and those narratives tend to range in size from somewhere around a hundred words to, perhaps, a few thousand words in length. The result is that any current model or notion of shape simply has allowed the wrong “figures figure figures.” Put another way, there can be no shape of stories without these stories.