Teacher‐makers and teacher‐breakers: (Re)defining how status and safety influence trajectories into and away from teaching
Bibliographic record
Abstract
Abstract This paper uses empirical data from a longitudinal qualitative study conducted with aspirant teachers in England to propose (re)definitions of the concepts of ‘status’ and ‘safety’ as a framework with which to understand and improve teacher recruitment. These (re)definitions develop from analyses of data tracking 13 aspirant teachers throughout their education from age 10 and into their first career at age 22. Analyses using an identity lens demonstrate that teaching was constructed as a profession which afforded both respect (status) and minimal risk (safety) by all aspirant teachers in this study. These constructions, however, were relative and transitory; and were strongly shaped by participants' ages as well as the social inequities they experienced. Those who chose to pursue teaching by age 22 maintained or returned to their constructions of teaching as both high in status and safety. These findings suggest that the reasons why some people do not become teachers are not the same reasons why people report leaving teaching. The paper ends with recommendations for how to increase our focus on status and safety in future research and practice, as well as a call for future research to involve aspirant teachers.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".