Bibliographic record
Abstract
Abstract Policy implementation reflects a complex change process where government decisions are transformed into programs, procedures, regulations, or practices aimed at social betterment. Three factors affecting contemporary implementation processes are explored: networked governance, sociopolitical context and the democratic turn, and new public management. This frame of reference invites evaluators to consider challenges present when evaluating macrolevel change processes, such as the inherent complexity of health and social problems, multiple actors with variable degrees of power and influence, and a political environment that emphasizes accountability. The evaluator requires a deep and cogent understanding of the health or social issues involved; strong analysis and facilitation skills to deal with a multiplicity of values, interests, and agendas; and a comprehensive toolbox of evaluation approaches and methods, including network analysis to assess and track the interconnectedness of key champions (and saboteurs) who might affect intervention effects and sustainability. © Wiley Periodicals, Inc., and the American Evaluation Association.
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 | 0.454 | 0.696 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.028 | 0.024 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".