1 EXPLAINING THE EXISTENCE OF POLICY NETWORKS BY MEANS OF A PUBLIC CHOICE APPROACH By
Bibliographic record
Abstract
2 Political scientists who study policy making and policy change by means of an inductive approach often depict the process by which governments make their policies as being subdivided into a series of networks or communities. The main reason why the policymaking process is often depicted by political scientists as being subdivided into a series of networks or subsystems is that they have observed that it is not always the same interest groups that participate to the process by which a given government makes its policies. More precisely, political scientists have observed that policies that concern different fields or issues are usually made by a given government with the participation of different interest groups. For example, in the case of the Canadian federal government, it has been observed that the interest groups that usually participate to the process by which this government makes its policies concerning air transport are totally different from the ones that usually participate to the process by which this government makes its policies concerning the financial sector or the telecommunication sector, etc. The observations that political scientists have made concerning the behavior
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".