Dataset of the effect of difficulty messaging on academic cheating in middle school Chinese children
Bibliographic record
Abstract
The present dataset was reported in a paper entitled “Effects of test difficulty messaging on academic cheating among middle school children” [1]. It reports the findings of an experimental study that used a naturalistic math test-taking paradigm to assess children's academic cheating behavior under different test difficulty messaging conditions. The participants were Grade 8 middle school children (N = 201). The primary dependent measures were whether each participant spontaneously decided to cheat (presence of cheating), and among participants who cheated, the specific number of test items on which they cheated (extent of cheating). We used logistic regression, ANOVA, and Pearson correlation to assess whether various predictor variables (e.g., conditions) predicted the presence of cheating or the extent of cheating. This dataset should be of interest to researchers who are interested in the development of moral behavior in children generally, and academic dishonesty in particular.
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How this classification was reachedexpand
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".