Trends and directions in Canadian policy analysis and policy advice
Bibliographic record
Abstract
Conducting policy analysis and giving policy advice are the art and craft of ‘speaking truth to power’—an expression commonly espoused in the Canadian public administration and governance literature (Dobell, 2015; Good, 2003; Savoie, 2003; Zussman, 2015). Over time, however, the concepts of truth and power in relation to policy advice have changed, as have ideas such as policy capacity, and their meanings therefore need investigating in the contemporary context. Nothing is more political, organizational, and relational than doing policy work in and for the state. Of course, rationality is important in policy development and decision-making (Azzie, 2015; Pal, 2014). Yet the subtle craft of policy advice in public service settings is typically characterized by ambiguous goals, multiple roles and structures, resource constraints, uncertain outcomes, and competing interests, ideas, and policy agendas. Moreover, the milieu in which the art and craft of policy advice is done has changed significantly in a number of respects over the last few decades in Canada, as in many other countries. This chapter reviews these changes and discusses their repercussions for policy advice as public service work. Under the speaking truth to power model, policy advice is, largely, a bipartite relationship involving public servants and executive politicians, with career officials offering advice to cabinet ministers. For some time now, however, it has been clear that a plurality of advisory sources exists, with an array of actors both inside and outside government offering various kinds of policy advice and analysis in various forms to decision-makers. This pluralism of policy advice has implications for the roles and relations of government analysts to governing politicians and their staffs, and to non-state actors in think tanks, lobby associations, and polling and consulting firms. It also raises disquieting issues of the capacity and influence of civil society organizations and clientele groups. To the point, changes in both the context and content of Canadian politics and government have produced a shift in the approach to policy analysis: public service policy advice has moved away from ‘speaking truth to power’ and toward what we may describe as “sharing truths with multiple actors.”
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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 | 0.051 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.058 |
| Science and technology studies | 0.031 | 0.023 |
| Scholarly communication | 0.037 | 0.013 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".