A subtle balance : expertise, evidence, and democracy in public policy and governance, 1970-2010
Bibliographic record
Abstract
A Subtle Balance critically reflects on major trends and enduring challenges over the last four decades of public policy and governance. During this time, a tension has existed between two aims for public decisions: that they be based on the best available evidence and analysis, and that they be fully democratic. This period has seen a continuing drive for more direct citizen engagement in decision-making and governments trying to address major policy issues through novel consultative and collaborative processes. In essays that offer detailed and novel insights into the recent history of specific issues in social policy, environmental policy, and processes of policy advice and decision-making, contributors elaborate on how these trends have played out in diverse areas of practice, what their consequences have been, and how specific institutional reforms could reset the requisite balance between expertise, evidence, and democracy in Canadian public policy. Inspired by the wide-ranging contributions to scholarship and practice of A.R. (Rod) Dobell, A Subtle Balance draws on the influences of distinguished scholars and sophisticated practitioners of public policy to assess recent changes in governance. Contributors include Martin Bunton, Barry Carin, Ian Clark, Rachel Culley, Rod Dobell, Lia Ernst, Jill Horwitz, John Langford, Justin Longo, Michael Prince, Harry Swain, Charles Ungerleider, Josee van Eijndhoven, Michael Wolfson, and David Zussman.
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.
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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".