School labelling as technology of governance: Problematizing ascribed labels to school spaces
Bibliographic record
Abstract
Central to contemporary educational reform in the United States are the procedures and techniques that hold schools accountable to the public and render them more visible. Labelling public school performance by ascribing identifiers which deem spaces of education either a success or a failure at educating its students is one way of identifying schools for consumers of education. This yields powerful representations of school quality; what is a “good” school and what is a “bad” school. These labels are problematic given the implications of labelling practices on identities, places, and the public's perception of school spaces. This article focuses upon the technique of labelling, and explores its implications through a critical analysis of the meaning and consequences of the politics of labelling with respect to contemporary education reform. I draw on insights from social theorists and consider primary findings from a survey of inner city public school teachers. These teachers provide views from the inside, a counter‐narrative of the labels ascribed to the schools in which they teach. The teacher perceptions of the label's impact on attitudes and behaviours highlight the need to contest and demystify hegemonic labels of contemporary reform.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.152 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".