Reframing Classroom Encounters: Teachers Making Sense of School Securitization
Bibliographic record
Abstract
This thesis explores the discourses available to teachers in navigating and making sense of their role in the securitization of high schools. My analysis is based on semi-structured interviews conducted with nine teachers working in urban schools in Toronto. Drawing on frameworks from post-colonial, critical race, and urban education studies, I argue that school securitization is not just complicated by racism, but structured and enabled by it. While there is an urgent need to resist the implementation of particular security and surveillance measures that intensify the targeted disqualification of racialized youth, it is equally if not more important to uncover and resist the ways that racial thinking organizes a much wider range of classroom encounters and pedagogical practices. I urge teachers to interrogate their investments in the categories and subject positions that race thinking makes available, including those that are desirable and pleasurable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".