Paper Presented to the Annual General Meeting of the
Bibliographic record
Abstract
This paper begins the analysis of complex multi-actor, multi-round decision-making processes in Canadian public policy formation. After setting out the notion of a decision-making style and its constitutive elements, the paper identifies research into complex multi-actor, multi-round decisions as a serious lacuna in the literature on decision-making, despite the fact that this type of decision-making is extremely common in public policy-making circumstances. The paper attempts to advance research in this area through the analysis of five cases of complex decision-making in Canada over the period 1995-2005, dealing with: amendments to the Indian Act, the creation of Species-at-risk legislation, alterations to the Bank Act, the extension of Privacy legislation to the private sector and efforts to develop a Free Trade of the Americas agreement (FTAA). A database of actor interactions in these four areas is constructed from on-line newspaper and media index services which establishes that (a) multiple rounds are a common feature of Canadian policy-making; (b) actor behaviour and activity is correlated with these rounds; and (c) significant, but predictable, variations exist in government and non-governmental actor behavior in
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.370 | 0.111 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".