Bibliographic record
Abstract
Although Bruce Doern's doctoral dissertation and first book examined Canadian science policy through the lens of the National Research Council, and his continued stature as a foremost Canadian and international scholar of science and government, 1 his first significant set of original ideas focused on the policy roles of central agencies.His work focused primarily on Canada, but the Canadian experience at the time was not unique.His ideas thus had international relevance.Bruce Doern ventured into the so-called "black box" of internal government decision-making, thus departing from the academic fashion of the times that focused almost exclusively on what David Easton, then the preeminent political science guru, had labeled the "input" side of the "political system." 2 The Eastonian framework of "systems analysis" paid great attention to the input side of the political process, in large part because this was the transparent dimension of politics and this was where one could examine political "behaviour," even using quantitative methods, in contrast to traditional studies that focused on institutions.Here were to be found political parties, interest groups, public opinion, and elections (constituting both "demand" and "support" dimensions of inputs).The "output" side of the political systems -the public policies and programs of government -was, of course, also transparent, but it was rarely the concern of political scientists, except in the sense of public policy constituting the outputs of political behaviour on the input side of the equation.Easton situated governmental institutions and processes between inputs and outputs.These institutions and processes were characterized as the "black box" of the decision-making
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".