Beyond Instrument Choice: Micro-Level Policy Design in Manitoba's Child Care System
Bibliographic record
Abstract
heories of public policy development have attempted to systematize the process by which governments define, design, implement and evaluate state activity (or inactivity).A crucial step within that development cycle is policy design, meaning the development of policy options and their alternatives (Howlett 2014).Although policy design can be said to encompass a variety of elements, "including policy goals, objectives and aims, as well as policy means, tools and their calibrations" (Howlett 2014:194), emphasis within the literature tends to centre around the question of instrument choice.This focus of activity is demonstrated in many ways: by defining policy instruments, through attempts to create instrument typologies or classifications, and the theorizing that marries instruments to policy goals.The prominence of instrument choice within the policy design literature is perhaps unsurprising.It is a laudable pursuit to provide greater potential for the success of policy interventions by arming practitioners with a toolbox of instruments whose characteristics and applications are defined.And it is not the purpose of this paper to discount the applied benefits that have come from such pursuits.It is, 1
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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 | 0.044 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".