Visual imagery and STEM occupational attainment: Gender matters
Bibliographic record
Abstract
Science, technology, engineering and mathematical (STEM) occupations are crucial for economic growth and individual financial stability, yet there is a STEM labour shortage, particularly among women. We examined how individual differences in visual imagery relate to characteristics of STEM occupations — specifically those requiring computational abilities. In a discovery cohort of 2357 online participants, we found that — consistent with prior research — spatial thinking was positively associated with STEM occupations for both males and females. Object imagery (mnemonic vividness), however, was negatively associated with STEM occupations that require computational thinking, possibly because efficient analytical reasoning abilities associate with low object imagery. This negative association was present for males, but not for females. We extended these findings to a sample of 1891 individuals with aphantasia (congenitally low imagery) and a sample of 186 university undergraduates. These results suggest that the well-known influence of spatial imagery is evident across genders, whereas an independent influence of non-spatial and non-visual abstract analytic abilities on computational STEM professions is confined to males. These findings have implications for policy in fostering careers in STEM, particularly for females.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".