Rethinking medicalization: unequal relations, hegemonic medicalization, and the medicalizing dividend
Bibliographic record
Abstract
Medicalization is an important theory that has been subject to numerous debates. Drawing on three varied datasets, we forward a relational approach to medicalization that responds to critiques while aiming to reinvigorate the theory with new concepts and questions. In contrast to prior process-based work, our relational approach argues that medicalization is best understood as an action or activity undertaken by specific groups or actors. We further suggest that unequal relations characterize medicalization. Specifically, we argue that 1) groups or actors receive a benefit from participating in medicalization, which we call the medicalizing dividend and, 2) an actor/group occupies a hegemonic position in medicalizing relations, reaping the largest dividend and constraining other actors. While we assert that pharmaceutical companies are currently hegemonic, we argue that their hegemony is not indefinite. We discuss how our approach facilitates links between medicalization and other theories, while outlining future steps for medicalization research.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.141 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.011 |
| 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".