New policy tools and traditional policy models: better understanding behavioural, digital and collaborative instruments
Bibliographic record
Abstract
The study of policy tools has been undertaken for several decades. This work has isolated and examined many different types of instruments or levers utilized by governments to implement their policies and examined in detail how they are arranged into mixes, packages, or portfolios of tools. However, recent developments in society and technology have highlighted the potential to use new or previously underutilized policy instruments for both traditional tasks and to address new challenges associated with emerging technologies and other contemporary issues. These tools include social media platforms, collaboration, behavioral insights, and data-driven approaches to policy-making and policy design using big data and artificial intelligence, among others. Like any other tool, however, each of these new tools has its strengths and weaknesses. This article addresses the promises and pitfalls of these new kinds of tools and assesses how their deployment and effectiveness can be understood using typologies and concepts developed to deal with traditional policy instruments.
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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 | 0.030 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.064 |
| Scholarly communication | 0.030 | 0.073 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".