Economic, cultural, and science capital: Familial factors shaping access to STEM outreach programs for children and youth
Bibliographic record
Abstract
The populations of engineering students and practicing engineers in Canada do not reflect the larger national or provincial populations in socioeconomic status (SES), gender, race, ethnicity, or Indigeneity. Outreach initiatives, such as STEM (Science, Technology, Engineering, and Mathematics) summer camps inclusive of underrepresented groups, aim to tackle these persistent dynamics of privilege and marginalization. STEM outreach programs continue to grow rapidly in all Canadian provinces and territories and have been shown to increase STEM identity and self-efficacy. However, young people’s likelihood of participating in extracurricular activities is organized by social inequality, particularly socioeconomic status. In collaboration with a STEM outreach program connected to a large Canadian research university, we ask whether family SES is associated with young people’s enrollment in STEM summer camps. Through a survey of campers’ families (n=196), we document household income, parents’ educational and professional backgrounds, home STEM learning environment, and parents’ support for and perceptions of STEM education and careers. Results indicate that many outreach participants come from families with high economic, cultural, and science capital. This research suggests there may be potential for STEM outreach programs to further target groups whose underrepresentation in STEM outreach is connected to SES.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".