Bibliographic record
Abstract
In response to "Evidence-Based Policy Prescription for an Aging Population," by Chappell and Hollander, this paper proposes that efforts be made to execute strategies to build the political momentum and public support necessary for concrete action toward achieving the recommended policies. It also suggests the implementation of knowledge translation strategies to assist in disseminating and integrating existing successful programs across the wider health system. Finally, this paper proposes a concerted and robust mobilization of forces in order to move from evidence-based agenda setting into active policy implementation. A key element of this transition involves placing greater emphasis on interest group activation and public policy deliberation. Such a focus would enable consensus between policy makers, decision-makers, interest groups and the public, garnering the political traction necessary to allow for the implementation of healthy public policy that best serves the needs of an aging population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.024 | 0.043 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.116 | 0.149 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".