Improving the Generalizability of Behavioral Science by Using Reality Checks: A Tool for Assessing Heterogeneity in Participants’ Consumership of Study Stimuli
Bibliographic record
Abstract
In attempting to draw bigger conclusions, researchers in psychology open their labs to more diverse groups of people. Yet even the most far-reaching theories must be tested with specific stimuli, materials, and methodology. To the extent that a study’s stimuli are familiar beyond the lab to groups of people writ large, an experiment is said to have mundane realism—a type of external validity. We propose that an experiment’s stimuli will vary in their relevance to each individual participant (such as how much they consume the stimuli outside the lab) and can be assessed using a tool: reality checks. We found that accounting for a study’s mundane realism, at the individual level, significantly altered a study’s results—which we found to be the case in testing well-established findings in psychology and behavioral economics. Our work suggests that measuring mundane realism (in addition to creating it) is a useful way of testing effects in psychology among the participants for whom the studies’ scenarios and decisions will matter most outside of the lab.
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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 | 0.244 | 0.518 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".