Assessing Instrument Mixes: Methodological Issues in Contemporary Implementation Research
Bibliographic record
Abstract
Theories of policy instrument choice have gone through several ‘generations ’ as theorists have moved from the analysis of individual instruments to comparative studies of instrument selection and the development of theories of instrument choice within implementation ‘mixes ’ or ‘governance strategies’. Current “next generation ” theory on policy instruments centres on the question of the optimality of instrument choices. However, empirically assessing the nature of instrument mixes is quite a complex affair, involving considerable methodological difficulties and conceptual ambiguities related to the definition and measurement of policy sector and instruments and their interrelationships. Using materials generated by Canadian governments, this paper examines the practical utility and drawbacks of three techniques used in the literature to inventory instruments and identify instrument ecologies and mixes: the conventional ‘policy domain ’ approach suggested by Burstein (1991); the ‘programme ’ approach developed by Rose (1988); and the ‘legislative ’ approach used by Hosseus and Pal (1997). The paper suggests that all three approaches must be used in order to develop even a modest inventory of policy instruments, but that additional problems exist with availability and accessibility of data, both in
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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 | 0.615 | 0.804 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.021 | 0.034 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.026 | 0.040 |
| Open science | 0.010 | 0.022 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".