Spatial anxiety and spatial ability: Mediators of gender differences in math anxiety.
Bibliographic record
Abstract
Females tend to be more anxious than males while engaging in mathematics, which has been linked to lower math performance and higher math avoidance. A possible repercussion of this gender difference is the underrepresentation of females in STEM fields (science, technology, engineering, and math), as math competencies are an essential part of succeeding in such fields. A related, but distinct, area of research suggests that males tend to outperform females in tasks that require spatial processing (i.e., the ability to mentally visualize, rotate, and transform spatial and visual information). Interestingly, factors from the spatial processing domain (spatial ability and spatial anxiety) are important in explaining gender differences in math anxiety. Here, we examined three types of spatial anxiety and ability (imagery, navigation, and manipulation), as well as math ability, as mediators of gender differences in math anxiety. Undergraduate students (125 male; 286 female) completed assessments of their general level of anxiety, their math anxiety, and their spatial anxiety. They also completed a series of tasks measuring their mathematical skill, their spatial skills, and basic demographics. Results suggest that manipulation anxiety and ability, navigation anxiety, and math ability explained the gender difference in math anxiety, but manipulation anxiety was the strongest mediator of this relation. Conversely, all other measures did not explain the gender difference in math anxiety. These findings help us better understand the gender difference in mathematics, and this is important in reducing the gender gap in STEM fields. (PsycInfo Database Record (c) 2022 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".