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Record W2987090273 · doi:10.1016/j.bodyim.2019.10.006

Looking to the future: Priorities for translating research to impact in the field of appearance and body image

2019· article· en· W2987090273 on OpenAlexafffund
Melissa Atkinson, Nicola Marie Stock, Jessica M. Alleva, Glen Jankowski, Niva Piran, Sarah Riley, Rachel M. Calogero, Alex Clarke, Nichola Rumsey, Amy Slater, Phillippa C. Diedrichs, Heidi Williamson

Bibliographic record

VenueBody Image · 2019
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsWestern UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaNederlandse Organisatie voor Wetenschappelijk OnderzoekScar Free FoundationUnilever
KeywordsPsychologyField (mathematics)Image (mathematics)Artificial intelligenceComputer scienceMathematics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.036
metaresearch head score (Gemma)0.089
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.964
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.089
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.004
Science and technology studies0.0050.010
Scholarly communication0.0170.019
Open science0.0040.007
Research integrity0.0090.016
Insufficient payload (model declined to judge)0.0260.003

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 head0.026
GPT teacher head0.421
Teacher spread0.394 · 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.

Study designTheoretical or conceptual
DomainEvaluation
GenreCommentary

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

Citations31
Published2019
Admission routes2
Has abstractno

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