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Record W4404964504 · doi:10.1080/15248372.2024.2431106

Tools of the Trade: A Guide to Sociodemographic Reporting for Researchers, Reviewers, and Editors

2024· article· en· W4404964504 on OpenAlex
Leher Singh, Mihaela Barokova, Marina Bazhydai, Heidi A. Baumgartner, Laura Franchin, Jessica Elizabeth Kosie, Casey Lew‐Williams, Paul Okyere Omane, Tilman Reinelt, Tobias Schuwerk, Mark Sheskin, Mélanie Söderström, Yang Wu, Michael C. Frank

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Cognition and Development · 2024
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsThe Scarborough HospitalUniversity of TorontoUniversity of Manitoba
Fundersnot available
KeywordsPsychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

In recent years, psychological researchers have been heavily criticized for generalizing broadly from narrow samples, a concern that intersects with questions about the validity, reproducibility, replicability, and generalizability of the psychological literature. One issue is the limited reporting of participants’ identities, backgrounds, and lived experiences. To address this issue, several journals have begun to require greater reporting of participants’ sociodemographic information. In this article, we address both challenges and considerations with respect to sociodemographic reporting for researchers, reviewers, and journal editors. We provide guidance for recording, evaluating, protecting, and interpreting sociodemographic data.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.124

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.276
GPT teacher head0.466
Teacher spread0.190 · 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