The relation between spatial and mathematical abilities: Potential factors underlying suppression
Bibliographic record
Abstract
Two experiments examined possible factors underlying the finding that grades in mathematics act as a suppressor variable in the relation between spatial abilities and gender. Specifically, the role of reading abilities was investigated in Experiment 1 by using English grades as a measure of these abilities. Experiment 2 was based on the notion that time pressures are involved at some level in both spatial performance and mathematics grades. The influence of this factor was examined by administering a spatial task with or without time limit and examining the suppression effect in both conditions. In both experiments, participants completed the Mental Rotations Test (MRT) as a measure of spatial ability. In Experiment 1, all participants were tested with limited time to complete the MRT and they were required to report their high school course grades in mathematics and English. Results revealed that both grades in high school mathematics and English produced significant suppression. However, the amount of suppression produced by each measure was similar. Therefore, the prediction that suppression would be greater with English than with mathematics grades was not supported. Experiment 2 involved testing in groups or individually with or without time limits on the MRT, whereas the Water Level Test was administered untimed, and only grades in mathematics were obtained from participants. Results supported the prediction that the suppression effect is greater when time limits are applied than when they are not. Implications of the results for an explanation of the observed suppression are discussed. Emphasis is placed on the difficulties inherent to the identification of factors underlying suppression.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".