Why Can’t a Student Be More Like an Average Person?: Sampling and Attrition Effects in Social Science Field and Laboratory Experiments
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.
Bibliographic record
Abstract
In the social sciences, the use of experimental research has expanded greatly in recent years. For various reasons, most experiments rely on convenience samples of undergraduate university students. This practice, however, might endanger the validity of experimental findings, as we can assume that students will react differently to experimental conditions than the general population. We therefore urge experimental researchers to broaden their pool of participants, despite the obvious practical difficulties this might entail with regard to recruitment and motivation of the participants. We report on an experiment comparing the reactions of student and non-student participants, showing clear and significant differences. A related problem is that differential attrition rates might endanger the effects found in long-term research. We argue that experimental researchers should pay more attention to the characteristics of participants in their experimental design.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it