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Record W4327732049 · doi:10.31234/osf.io/3beqr

Unattractive, corrupted, and culpable feared self-themes: Expanding our understanding of self-concept in relation to eating pathology

2023· preprint· en· W4327732049 on OpenAlexafffund
Samantha Wilson, Müge Acar, Ruqayya Hirji, Sarah Elizabeth Racine

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcGill University
FundersCanada Research Chairs
KeywordsPsychologyEating disordersFeelingClinical psychologySelfDisordered eatingRelation (database)Construct (python library)Relevance (law)Social psychology

Abstract

fetched live from OpenAlex

There is growing support for fear of self as a transdiagnostic construct implicated in obsessive-compulsive disorder (OCD) and eating disorders. However, few studies have examined how perceived proximity to a feared self may be associated with the relationship between fear of self and eating pathology. A community sample of women (N = 290) completed an online questionnaire battery. Eating pathology was positively associated with the feared unattractive self and, to a lesser extent, with feared corrupted and culpable selves, suggesting the relevance of a range of feared selves in eating pathology. There was a significant interaction between fear of the unattractive self and perceived proximity to this feared self (operationalized as ‘feeling fat’) in relation to eating pathology.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.087
GPT teacher head0.367
Teacher spread0.281 · 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 designObservational
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

Citations2
Published2023
Admission routes2
Has abstractyes

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