Children’s Aspirations Towards Science-related Careers
Bibliographic record
Abstract
Abstract Science-related careers are often considered to be less accessible by many children. More research is needed to distinguish any influences from different aspects of life so that support and/or interventions can be focused to help mitigate any disadvantage and inaccessibility. In order to gain greater understanding of constraints or influences on children’s aspirations towards science-related careers, a nationally-representative cohort of 7820 children in England was considered at age 11 and at age 14. At age 11, children’s science-related career aspirations were predictively associated with their ethnicity, gender, and science self-confidence, and also (at lower magnitudes) with the children’s motivation towards school and indicators of family advantage. At age 14, children’s aspirations were predictively associated with their prior aspirations (as of age 11), science self-confidence (as of age 14), and again with ethnicity and gender. Notably, these gender and ethnicity associations varied when considering specific aspirations towards science/engineering and towards medicine/health: boys were more likely to express science/engineering aspirations and less likely to express medicine/health aspirations; concurrently, children from some minority ethnic backgrounds were less likely to express science/engineering aspirations and more likely to express medicine/health aspirations. Overall, the findings suggest that support after age 11 still needs to promote the feasibility of different science careers for all children.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".