Friendship networks predict girls’ STEM fit and interest through subjective belonging
Bibliographic record
Abstract
Girls report lower belonging in STEM (science, technology, engineering, mathematics) than boys, which may carry costs for girls’ later STEM participation. We hypothesized that being socially included within a STEM context supports feelings of belonging—which then contributes to stronger intentions to pursue STEM, especially for girls. To investigate, we recruited girls and boys (N = 1,330; Mdn age = 12; 41% White, 35% East Asian) attending week-long Canadian STEM summer camps. We gathered precamp and postcamp STEM intentions (fit and interest), plus postcamp objective social inclusion and subjective belonging (with distinct metrics computed for female vs. male peers). Consistent with previous findings, girls had lower STEM intentions than boys. In addition, we found that, for girls, being more socially included (particularly by male peers) was associated with stronger STEM intentions, mediated by subjective belonging. For boys, social inclusion (via belonging) was less predictive of STEM intentions. These results highlight how childhood friendships may impact early intentions to pursue STEM education and careers, especially for girls.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".