Blue is For Boys, Science is For Boys, What's For Girls? A Critical Look at Gender Constructs in Science Toys
Bibliographic record
Abstract
Canadian born women are severely underrepresented amongst science students and researchers in Canada.This is due in part to the cultural construction of science as "unfeminine" and "for boys."Educational toys are often the first avenue in which children are introduced to and engage with scientific culture.Parents have traditionally used gender symbols in toys to teach children gender roles, appropriate values and actions.The current study investigates the possibility of science toys as a socializing agent for gendered beliefs about science.It does so, by completing a content analysis and qualitative analysis of the pairing of gender and science symbols within 400 science toys.This analysis found that science toys contained a substantial number of masculine symbols.Overt symbols such as boys alone on the package occurred three times more often than girls.Feminine mentors were symbolically annihilated among all disciplines making up less than 10% of all scientists.Covert symbols such as masculine shades, themes and child absent from the package were more common than gender neutral or feminine themes.Finally, the use of masculine symbolism differed by discipline, pointing towards several sub-cultures in science representing different degrees of adherence to masculine symbolism.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".