Institutional Ethnography as Critical Policy Analysis
Bibliographic record
Abstract
The use of the term health equity (HE) has increased dramatically in the past decade in Canada and globally. However, there is limited evidence that explosive growth in the use of the term is reflected in HE policy outcomes. In this paper we argue that the methodology of institutional ethnography (IE) is useful in understanding how policy outcomes (including, notably, policy inaction) are constructed. Applying IE methodology in critical policy analysis (CPA), we analyze how the discursive utilization of HE neutralizes effective policy change. The point of our analytical approach is to explain how three complex areas of investigation (HE, IE, CPA) may be usefully integrated to enhance policy action to tackle the structural, root causes of health inequities. We demonstrate how an integration of IE in CPA provides a deeper understanding of how the political forces shaping policy outcomes manifest themselves in discursive relationships and textually mediated sites of power.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".