THE ROLES OF EXPERIENCE, GENDER, AND INDIVIDUAL DIFFERENCES IN STATISTICAL REASONING
Why this work is in the frame
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Bibliographic record
Abstract
We examine the joint effects of gender and experience on statistical reasoning. Participants with various levels of experience in statistics completed the Statistical Reasoning Assessment (Garfield, 2003), along with individual difference measures assessing cognitive ability and thinking dispositions. Although the performance of both genders improved with experience, the gender gap persisted, with males outperforming females across all experience levels. A confirmatory structural equation model assessing the degree to which cognitive ability, thinking dispositions, and gender account for statistical reasoning performance supported the idea that differences in statistical reasoning are not uniquely a matter of cognitive ability. Rather, gender was found to influence statistical reasoning directly, as well as indirectly through its influence on thinking dispositions. First published November 2017 at Statistics Education Research Journal Archives
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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 | 0.005 | 0.055 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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