Intersectionality, Policy‐Oriented Research and the Social Relations of Knowing
Bibliographic record
Abstract
Researchers attempting to improve knowledge for policy making look to intersectionality to theorize the important, yet poorly understood constitutive elements of people's lives and experiences. As argued here, certain epistemological and ontological issues, while much debated, remain a problem in intersectional and other social research. This paper introduces Dorothy E. Smith's analytic approach, institutional ethnography, arguing that its use avoids reliance on categories that objectify people and, instead, explicates the social relations that rule people's knowing and doing. In institutional ethnography, people are understood to conduct and experience their lives within discursively organized social relations, coordinating their activities with institutions and the political economy, more broadly. Addressing how the latter implicates policy‐oriented activism, an illustration from research in healthcare is used, demonstrating how in accounting empirically for ‘what actually happens’, an institutional ethnography makes visible the relations of knowing that link research subjects and researchers, too, into an institution's ruling purposes.
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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 | 0.040 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.016 | 0.172 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".