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Record W2959992185 · doi:10.3167/screen.2018.030202

Screening the Slob

2018· article· en· W2959992185 on OpenAlexaff
Mackenzie Edwards

Bibliographic record

VenueScreen Bodies · 2018
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsYork University
Fundersnot available
KeywordsMultitudeArchetypeIdeologyDepictionPolitical sciencePsychologyArtPoliticsLiteratureLaw

Abstract

fetched live from OpenAlex

This article explores the archetype of the slob, narrowing in on its depiction in the episode “King-Size Homer” from The Simpsons (1989–), the long-running satirical animated series created by James L. Brooks, Matt Groening, and Sam Simon. More than simply analyzing what constitutes the slob, this article focuses on how the slob operates. Attention is paid to the enmeshing of fatness and disability. The undercurrent of neoliberal ideology that runs through the episode is made apparent. The article works intersectionally to understand the slob as being someone who is abject in a multitude of ways. Finally, it considers the topic of disidentification and the possibilities that it opens up for a better analysis and understanding of the episode. And throughout the article, the key themes of failure and the pursuit of failure are explored.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.823
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.007

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 head0.142
GPT teacher head0.482
Teacher spread0.340 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

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