Bibliographic record
Abstract
Introduction In this volume, in the context of the means and mandate of the International Library of Policy Analysis book series, we hope to help lay the foundations for a more systematic and comparative understanding of policy analysis practices in Canada than has hitherto been available, and thereby contribute to enhanced performance and utilization of the analytical work undertaken both within Canadian governments and in those organizations that wish to influence Canadian public policy. The academic literature has often distinguished between policy study and policy analysis and the essays and arguments found in this volume fit at the nexus between these two approaches. The former term refers to the study of policy—that is, how to better understand policymaking processes, actors and outcomes—while the latter refers to the study of policymaking for policy—that is, to improve practice. Policy studies, the subject of an earlier volume by the editors (Dobuzinskis, Howlett & Laycock, 1996), is conducted mainly by academics, and relates to the study of ‘meta-policy’ or the overall nature of the activities of the state. It is generally concerned with understanding the development, logic and implications of state policy processes and the models used by investigators to analyze those processes. “Policy analysis,” by comparison, refers to applied social and scientific research—but also involving more implicit forms of practical knowledge—pursued by government officials and non-governmental organizations usually directed at designing, implementing, and evaluating policies, programmes and other courses of action adopted or contemplated by public authorities. In a general sense, it is ‘the disciplined application of intellect to public problems’ (Pal, 2010, p. 15). Policy analysis in this sense can be traced back to the wartime planning activities and ‘scientific management’ thinking of the mid-twentieth century. It was then more widely applied in the 1960s and 1970s to large-scale social and economic planning processes in areas such as defence, urban re-development and budgeting—especially as a result of the implementation of the Planning Programming Budgeting System (PPBS) in the United States, Canada and other countries (Heineman et al., 1990; Garson, 1986; see also Lindblom, 1958; Dobuzinskis, 1977; Wildavsky, 1979; Starling, 1979). Since then, as chronicled by the country volumes in the ILPA series, ‘policy analysis’ has spread through the globe with professional associations and dedicated schools and teaching programmes developing in many countries.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.028 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".