'The Deep Slumber of Decided Opinion': How Teachers and School Administrators Understand Controversial Issues Policy
Bibliographic record
Abstract
Recognition that social and global issues are important to education has re-ignited interest in policy approaches to controversial and sensitive issues(CI).Without a policy framework for CI education, educators become 'de-skilled' by their working conditions. This qualitative study researches teachers and school administrators' understanding of CI policy (CSI 2003) using a theoretical framework structured on critical democracy and a conceptual lens of critical discourse and narrative analyses. Research findings reveal CSI lacks a cogent conceptual framework; it supports curricula and board policies. Some school administrators use CSI to support the system against community-based challenges to policy and programs. In this way, the process of CSI is not democratic. Further, a critical discourse analysis reveals deeply-embedded contradictions through competing voices for authority. Teachers report feelings of fear and experiencing surveillance in conditions of inadequate and inequitable CI policy support. The findings locate inattention to students' roles is defining what (and how) CI enter the classroom. The research findings sharpen our knowledge of local-level policy activity among users. For these and other reasons, the work censures CI policy for 'white privilege' and 'liberalism.'From a theoretical perspective, the thesis asserts the need for more work on the intersections of critical policy, critical democracy and citizen engagement in policy processes.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".