The biomedicalisation of ageing policies: a comparative analysis of public administration and expertise in Canada, the United States and Sweden
Bibliographic record
Abstract
This article explores how the biomedicalisation of ageing permeates the fields of public administration and public policy. We posit that the biomedicalisation of ageing policies depends strongly on: (1) the institutionalisation of ageing policy, both with regard to ministerial responsibility for programmes targeting older adults and the construction of ageing as a healthcare policy problem within the state apparatus; and (2) the dominant presence of health experts and professionals in the policy-making process in the field of ageing. We present a comparative analysis featuring three countries (Canada, Sweden and the US) with different administrative configurations and policy mixes in relation to older adults. We conclude that the biomedicalisation of ageing expertise is strongest in Canada and the US, and weakest in Sweden. The delegation of long-term care responsibility to municipalities and the strong commitment to develop and include social science expertise explains the Swedish outcome. The article provides illustrations as to why this distinction matters in policy making and in the day-to-day lives of older adults, and why it should be explored in other countries around the globe.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".