Who Aspires to Become a Teacher? Findings from a Cohort Study Tracking Young People from Age 10/11 to Age 21/22
Bibliographic record
Abstract
Against a backdrop of severe and long-standing teacher shortages, in this paper we present analyses of over 60,000 survey responses collected via a cross-sectional cohort study that sampled young people in England over a period of 11 years; at ages 10/11, 12/13, 13/14, 15/16, 17/18, and 21/22. These methods allow us to explore how common teaching aspirations are amongst young people at different ages, and who aspires to become a teacher as a future career. Analysing both free-text and Likert-scale data, we find that many more young people express an interest in becoming a teacher than is reflected in teacher recruitment data, and that teaching aspirations are patterned by gender and ethnicity. Girls and young women, as well as young people who identified as White, were significantly more likely to be open to teaching than their peers. Our findings suggest that teaching is a common back-up, or second-choice career aspiration, and that many individuals who report an earlier interest in teaching do not go on to become teachers. We end the paper with reflections on how these findings might be used to increase and diversify teacher recruitment, as well as recommendations for future research.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".