Picking up the pieces: Sex differences in mechanisms of curve tracing.
Bibliographic record
Abstract
This study examined potential sex differences in the application of models of curve tracing, namely the pixel-by-pixel model, the bipartite model, and the zoom lens model. The purpose of this study was therefore to determine whether sex differences existed in terms of reliance on a particular model or whether the results of each sex could be best explained by one model. This was done by examining the combined data obtained by Voyer and MacPherson (2020), consisting of 420 participants, with 194 men and 226 women. We examined only the curve-tracing task data from that study and compared the fit of the different models as well as a possible interaction with sex of participants on the proportion of correct responses and response time. Overall, sex was a significant factor, with men showing better average accuracy and faster performance than women. On accuracy, we found that the pixel-by-pixel model provided the best fit for women, whereas the zoom lens model produced the best fit for men. On response time, the zoom model was the best predictor of response time for both sexes. The discussion elaborates on an account of these findings and on how our results might generalize to other visual-spatial tasks where a performance advantage for men is found. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".