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 growth of what some academics refer to as 'the policy analysis movement' represents an effort to reform certain aspects of government behaviour. The policy analysis movement is the result of efforts made by actors inside and outside formal political decision-making processes to improve policy outcomes by applying systematic evaluative rationality to the development and implementation of policy options. This volume offers a comprehensive overview of the many ways in which the policy analysis movement has been conducted, and to what effect, in Canadian governments and, for the first time, in business associations, labour unions, universities, and other non-governmental organizations. Editors Laurent Dobuzinskis, Michael Howlett, and David Laycock have brought together a wide range of contributors to address questions such as: What do policy analysts do? What techniques and approaches do they use? What is their influence on policy-making in Canada? Is there a policy analysis deficit? What norms and values guide the work done by policy analysts working in different institutional settings? Contributors focus on the sociology of policy analysis, demonstrating how analysts working in different organizations tend to have different interests and to utilize different techniques. They compare and analyze the significance of these different styles and approaches, and speculate about their impact on the policy process.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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