Self-Regulation, Exhortation, and Symbolic Politics Gently Coercive Governing?
Bibliographic record
Abstract
This chapter examines the power dynamics of seemingly gentle instruments of governing by the state.I do so by reviewing Bruce Doern's continuum of governing instruments arrayed by degrees of legitimate coercion, in particular his conceptualization of the least coercive instruments of self-regulation, exhortation, and symbolic policy outputs.My objectives are threefold.One is to revive interest in these aspects of Doern's model, which have received relatively little theoretical or empirical attention in contemporary Canadian policy studies.The second is to broaden our understanding of these three instruments beyond conventional interpretations in the field of policy studies.The third objective is to consider potential linkages between Doern's conception of these instruments and the ideas of analysts in other branches of the social sciences, with a view to suggesting future lines of theorizing and research by students of public policy and administration.Theoretically, the topic's significance relates to thinking beyond governments to a focus by scholars on governance and governmentality; to the growing research on non-governmental politics and civil society organizations; and to the activism, experiences, and knowledge of social groups which, until recently, political science and policy studies have tended to neglect.I challenge the notion that the legitimate coercion of a political community is located totally within the state, and I question the classic liberal democratic belief that beyond the exercise of state authority, beyond the continuum of governing instruments, personal liberty and freedom of choice by
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.034 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".