Learning Through Evaluation? Reflections on Two Federal Community-Building Initiatives
Bibliographic record
Abstract
Abstract: In recent years, the federal government has launched numerous pilot projects to tackle complex, localized policy problems through new modes of governance involving vertical engagement with community-based organizations and horizontal collaboration across departments. A key purpose of these time-limited projects is policy learning, with an emphasis on action research and stakeholder dialogue to inform future innovation. However, realizing the possibilities for learning through pilot projects requires evaluation frameworks sensitive to the particular challenges of collaborative and community-based policy making. Through comparative case study analysis of two recent federal pilot projects, we highlight tensions in prevailing approaches and explore strategies for better alignment of federal evaluation frameworks with the needs and capacities of local communities.
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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 | 0.193 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.053 | 0.032 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".