Policy Analytical Capacity and Policy Activities
Bibliographic record
Abstract
The study of policy process involves the study of
 policy actors - people involved in the development of public
 policy in a particular geographic area. This paper investigates
 policy actors in the context of Colorado climate and
 energy issues with a particular emphasis on the types and
 levels of their engagement in policy activities. The conceptual
 framework guiding this study centers on policy analytical
 capacity, the ability to acquire and use information in the
 policy process. High policy analytical capacity is expected to
 be associated with high levels, and more diverse kinds, of
 policy activities. The findings partly confirm the expectations.
 Actors from government and the non-profit sector
 report the highest policy analytical capacity and highest and
 most diverse range of policy activities. However, researchers,
 despite relatively high levels of policy analytical capacity,
 report involvement in just a few activities beyond conducting
 research. Actors with strong educational backgrounds in the
 physical sciences are more likely to be involved in conducting
 research whereas those with strong backgrounds in the
 social sciences are more likely to be involved in evaluating
 and appraising policies and working with the public. The
 conclusion contextualizes the findings by focusing on the
 relationship between technical and scientific complexity of
 climate and energy issues and the necessity for participating
 actors to possess high levels of policy analytical capacity.
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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 | 0.037 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".