Three competing interpretations of policy problems: tame and wicked problems through the lenses of population aging
Bibliographic record
Abstract
Abstract This contribution presents competing lenses of population aging as policy problems and it compares their impact on the treatment of policy problems. Three lenses are analysed: intergenerational, biomedical and social gerontological. The intergenerational lens treats population aging as a new form of class conflict along age groups. The social gerontological lens claims that population aging is first and foremost a social issue and it stands in opposition to the dominance of biomedical approaches that treat aging as a pathology. The presence of these three alternative conceptions of the policy problem is indicative of the complexity surrounding population aging and the importance of having divergent definitions of policy problems. Via an analysis of informal care giving in the Canadian context, this contribution also presents a comparison of the three lenses with a focus on the roots of these conceptualisations in various disciplines, their prevalence in various public organisations, and the policy consequences of their strength or weakness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".