The “lumpiness” thesis revisited: the venues of policy work and the distribution of analytical techniques in Canada
Bibliographic record
Abstract
Introduction: analytical techniques and policy analysis At its heart, policy analysis is what Gill and Saunders (1992, pp. 6–7) characterized as “a method for structuring information and providing opportunities for the development of alternative choices for the policymaker.” This involves providing information or advice to policy makers concerning the relative advantages and disadvantages of different policy choices (Mushkin, 1977; Wildavsky, 1979). Professional policy analysts employ many different types of tools in this work (Mayer et al., 2004; Colebatch et al., 2011). These tools are generally designed to help evaluate current or past practices and aid decision-making by clarifying or eliminating many possible alternative courses of action. In this sense, these policy tools play a significant role in policy formulation activity and potentially play a significant role in determining the content of policy outputs and thus policy outcomes (Sidney, 2007). As such they are a worthy subject of investigation in their own right. Unfortunately, however, generally speaking little is known about many of the practices involved in policy work (Colebatch, 2005; Colebatch, 2006; Colebatch & Radin, 2006; Noordegraaf, 2011) nor about the tasks and activities involved in policy formulation (DeLeon, 1992; Linder & Peters, 1990). That is, although many works have made recommendations and suggestions for how formulation should be conducted (Vining & Weimer, 2010; Dunn, 2004), very few works have studied how it is actually practised on the ground, and data is limited on virtually every aspect of the policy appraisal activities in which governments engage (Page, 2010; Page & Jenkins, 2005). Some progress has been made on this front in recent years. Nilsson, Jordan, Turnpenny and their colleagues have made considerable progress in, for example, mapping many of the activities involved in both ex post and ex ante policy evaluation (Nilsson et al., 2008; Hertin et al., 2009; Turnpenny et al., 2009). This has been joined by work done in Australia and elsewhere on regulatory impact assessments and other similar tools and techniques used in formulation activities (Carroll & Kellow, 2011; Rissi & Sager, 2013). In addition, more evidence has slowly been gathered in these countries and elsewhere on the nature of policy work and the different types practised in different situations by different actors (Mayer et al., 2004; Boston et al., 1996; Tiernan, 2011; Sullivan, 2011).
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.030 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".