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Record W2004361344 · doi:10.1632/pmla.2013.128.1.201

A Little Like Reading: Preference, <i>Facebook</i>, and Overwhelmed Interpretations

2013· article· en· W2004361344 on OpenAlexaboutno aff
Michael Cobb

Bibliographic record

VenuePMLA/Publications of the Modern Language Association of America · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicFreedom of Expression and Defamation
Canadian institutionsnot available
Fundersnot available
KeywordsLiteratureReading (process)WitchReignArtMidnightHistoryArt historyPhilosophyPsychoanalysisPsychologyLawPoliticsPhysics

Abstract

fetched live from OpenAlex

Somehow, of late I had got into the way of involuntarily using the word “prefer” upon all sorts of not exactly suitable occasions. And I trembled to think that my contact with the scrivener had already seriously affected me in a mental way. And what further and deeper aberration might it not yet produce? —Herman Melville, “Bartleby the Scrivener” (22-23) His brain was jerking forward likea bad slide projector. Hesaw the doorway the house the night the world and on the other side of the world somewhere Herakles laughing drinking getting into a car and Geryon's whole body formed one arch of a cry—upcast to that custom, the human custom of wrong love. —Anne Carson, Autobiography of Red (75) Like eyes that looked on Wastes— Incredulous of Ought But Blank—and steady Wilderness— Diversified by Night— Just Infinites of Nought— As far as it could see— So looked the face I looked upon— So looked itself—on Me— I offered it no Help— Because the Cause was Mine— The Misery a Compact As hopeless—as divine— Neither—would be absolved— Neither would be a Queen Without the Other—Therefore— We perish—tho' We reign— —Emily Dickinson, poem 693 Herman Melville, Anne Carson, and Emily Dickinson. These authors' bits of language just claimed me as I stared at some books on my office shelf, and I'm not sure exactly what to make of these passages except that I like them. So I'm listing them for you. You might also like them. I like many things, and in no particular order. For instance, here's what I “liked” one day, not long ago, on Facebook: a picture of the word Puppies! scrawled on a sidewalk; a New York Times story about the disorganization of the bicentennial of the War of 1812 (that war has a huge, nearly comical significance in my adopted country of Canada—did you know that Canadians burned down the White House?); an audio clip of Justin Bieber, featuring Busta Rhymes, singing “Little Drummer Boy”; my friend and colleague Jordan Stein's “vegan homo Thanksgiving” photo album; a posting by my “friend” “Emily Dickinson”; numerous updates about and images of the November 2011 pepper spraying of protesting students on the University of California, Davis, campus. I could go on and on, which is probably one of the reasons I, and millions of others, go on and on Facebook. Disorderly is the right word, but the likes are not quite random. People have generated these items, these virtual objects of interest, for rapid public consumption and, with the ubiquity of the “Like” button, for rapid public response. They (we) put stuff out there in part because we're showing off our preferences, or if not our preferences (even though they will be acknowledged with our liking) then at least things that interest us and (we hope) others. It's hard to know exactly what liking something on Facebook means because a like is nearly the same thing as an acknowledgment, something that says, “Yes, I clicked on this item, and it did not displease me.” And often people complain in comments that they wish there were variations on the “Like” button (“I want to express my anger with this piece of information—I wish there were a ‘Hate’ button”). Whatever our motivations or the nature of our interest in what we curate for the world on Facebook, these objects for consumption often go under the heading of like; so, like it or not, we're reading for like—we're doing a little like reading.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.012
Scholarly communication0.0100.009
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0340.013

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.265
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2013
Admission routes1
Has abstractyes

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Same venuePMLA/Publications of the Modern Language Association of AmericaSame topicFreedom of Expression and DefamationFrench-language works237,207