New policy tools and traditional policy models: better understanding behavioural, digital and collaborative instruments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it