Black male teachers, white education spaces: Troubling school practices of othering and surveillance
Bibliographic record
Abstract
The absence of male teachers in primary schools has been an ongoing concern for policymakers and schools in the UK, USA, Canada and Australia, and as schools have become more ethnically diverse so have concerns that the teacher workforce should reflect the communities it serves. Pre‐service teacher training plays a critical role in this aim, by identifying, recruiting, retaining and training those who demonstrate potential to become teachers in English primary schools. As one of a few studies to explore the racialised and gendered experiences of black male teachers in England, I adopt the use of critical race theory (CRT) to examine how black male teachers are characterised and constructed in white education spaces. Drawing on a larger study, this paper utilises counternarrative, a key precept of CRT, to draw attention to processes of exclusion, othering and surveillance through the experience of David (the main character). Interview and documentary data illuminate institutional processes of overt and covert racism, as well as racialised and gendered stereotyping. David’s story reveals how his voice is muted as it is woven into processes of othering, hyper‐surveillance and disciplinary power.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.040 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".