Methods for Advancing an Open, Replicable, and Inclusive Science of Social Cognition
Bibliographic record
Abstract
This chapter offers a toolkit of concrete methods and practices that researchers can use to advance open, replicable, and inclusive science. Part I describes how to calibrate confidence in a significant finding to the strength of evidence provided by that finding (e.g., understanding positive predictive value, assessing statistical power, creating effective preregistrations). Part II describes how to calibrate the scope of a study’s conclusions to the participants and stimuli sampled for that study (e.g., reporting more complete sample characteristics, writing constraints on generality statements, forming effective cross-cultural collaborations). Part III discusses the importance of approaching the science of social cognition from a range of vantage points and offers recommendations for improving diversity and inclusion in the field (e.g., building diverse and inclusive research teams, understanding positionality). Part IV offers strategies for researchers in different career stages to help change the field’s incentive structures to better support all of these practices.
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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 | 0.407 | 0.512 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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.
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".