Tools of the Trade: A Guide to Sociodemographic Reporting for Researchers, Reviewers, and Editors
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.369 | 0.629 |
| Meta-epidemiology (narrow) | 0.007 | 0.010 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.044 | 0.030 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.068 | 0.086 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".