Dangerous Childhood? Constructing Risk and the Governance of Teacher-Student Interactions
Bibliographic record
Abstract
Within the past two decades, elementary school boards and related professional bodies in Ontario, Canada have implemented several directives aimed at regulating teacher-student interactions. One reading of these proscriptive tools is that they buttress constructions of child-students as inherently vulnerable and in need of adult protection. A more thorough analysis reveals, however, that such directives construct teachers as both risky (threatening) and at-risk (threatened). In this paper I pay particular attention to the ‘Common Sense Tips’ offered to help teachers identify, manage and/or prevent sexual misconduct cases and allegations. While these documents might be read as reflecting increased social and political concern about the threats teachers pose to students, they are also awareness-raising tools that encourage teachers to be cognisant of their own and their colleagues’ risk of sexual misconduct allegations. Accordingly, these tools encourage teachers to govern their activities in ways that help to mitigate such risks. Such a reading allows us to call attention to the ways in which policies respond to and reinforce entrenched binary representations of adults/children, offenders/victims, subjects/objects, which themselves rely on, reproduce and sustain social inequalities. I conclude by offering some conjectures about the repercussions the reviewed mechanisms may have for teacher-student interactions within elementary school environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".