Locating Invisible Policies: Health Canada’s Evacuation Policy as a Case Study
Bibliographic record
Abstract
I describe an initial tool for revealing invisible policies. Invisible policies are made apparent by three criteria: allocation of resources, material impacts, and reactions. Allocation of resources can be economic, human, or otherwise. Material impacts are those that are tangible and can be described as having a physical impact in some manner. Finally, the reactions of those impacted by the policy, like agencies and scholars, provide a third lens through which these policies can be understood and identified. Using the three criteria, I reveal the long-standing “evacuation policy” as a genuine and authentic policy, which is currently applied to those First Nations populations falling under federal jurisdiction. My contribution to policy analysis is to provide another tool to close a gap in the literature with respect to the analysis of invisible policies. This paper won the Women’s and Gender Studies et Recherches Féministes (WGSRF) Graduate Essay Prize in 2014.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".