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Record W2092961807 · doi:10.1353/esc.2013.0036

Everybody Loves Imparfait: Academic Cultures of Imperfection

2013· article· en· W2092961807 on OpenAlexvenueno aff
Cecily Devereux

Bibliographic record

VenueEnglish studies in Canada · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsArtLiteratureHistory

Abstract

fetched live from OpenAlex

WHEN TWENTY-TWO-YEAR-OLD JENNIFER LAWRENCE fell On her way up the steps of the Dolby Theatre in Los Angeles in February 2013 to accept the Best Actress Oscar, it must have seemed to many of the program's viewers as it did to those in my house that the moment, while it may not have been not absolutely ruined, was certainly compromised. The fall itself was such a little thing, and Lawrence was charming in her gracious and honest reaction. Nonetheless, the fall became forever a part of the moment, a little interruption, a small event in the category of things we never want to happen to us (falling down in front of millions of viewers on live television) mixed in with the big event in the category of things that seem as if they would be pretty nice (winning an Academy Award--or, for that matter, any award). It was, or seemed to be, an imperfection, a bad thing adversely affecting a good thing, a flaw, a blot. But Lawrence's fall also made for a curiously productive and interesting moment. In the hyperproduced and somewhat banal Academy Awards ceremony, it seemed oddly and importantly real. It was so evidently unscripted and so ordinary that it threw into relief the extraordinary weirdness that is the Oscars. It probably made lots of people feel sympathetic things. It made Lawrence kind of loveable, not least as she brought about a kind of symbolic tear in the screen, on whose refracted surface most of us who watch the Academy Awards watch them. Popular culture makes much of such moments of imperfection in celebrity culture. Tabloids celebrate them or, at any rate, delight in finding opportunities to expose what are touted as the imperfections behind the seeming perfection of celebrities' (and especially actresses') skin, hair, bodies, relationships, lives. Perhaps, given the culture of tabloid exposure of flaws, it is not surprising that imperfection is central to so much Hollywood cinema: Lawrence won her Oscar, after all, for her role in a film, Silver Linings Playbook, that is focused on what is typically represented as imperfection across a number of registers--mental health, economic self-sufficiency, adulthood, families, marriages, houses. The film's happy ending is itself arguably imperfect: only a moment between the realities of unemployment, institutionalization, debilitating meds, and scabby ceilings. The short papers in this forum were presented at ACCUTE'S conference at the University of Victoria, 1-4 June 2013. Speakers in the panel were asked to think about imperfection with reference to the following questions: * What constitutes imperfection? How relative is its assessment? How do we recognize it? Define it? Measure it? * What are the politics of perfection? What does it mean to aspire to perfection? Or, for that matter, to aspire to imperfection? * How can perfection operate as a standard--in the arts? in everyday life? How much do aesthetics come into play in our assessment and valuing of perfection? * How do we engage with ideas of perfection in cultural representations such as reality television? To what impulses are such representations--of perfect homes, dates, bodies, wedding dresses--addressed? What does a critique of these ideas of perfection look like? * How do academics engage with, resist, or embrace imperfection? Is academic work in fact characterized by perfectionism, in writing, editing, presenting? How much too much do we do toward perfection? How do we deal with imperfection? * How much do we revert to and inhabit the imperfect tense, an index of what is never finished, always in process? * How do we represent the things that militate against perfection: illness, grief, travel, family responsibilities, power failures, lost files, missed texts, books we can't get, archives we can't visit, funding shortfalls, competition, filling out forms, applying for everything, trying to get the computer to work, making mistakes? …

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)0.027
metaresearch head score (Gemma)0.036
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0690.106
Scholarly communication0.0380.024
Open science0.0040.037
Research integrity0.0050.016
Insufficient payload (model declined to judge)0.0080.002

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 head0.025
GPT teacher head0.256
Teacher spread0.232 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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Same venueEnglish studies in CanadaSame topicSouth Asian Cinema and CultureFrench-language works237,207