Bibliographic record
Abstract
The twenty-sixth edition of How Ottawa Spends examines the policy initiatives, priorities, and initial spending of Martin's Liberals in an era where a political coronation seemed inevitable but high expectations had to be managed downwards almost immediately. Carleton University's School of Public Policy and Public Administration's annual study focuses on key issues, including Canada-US cross-border relations, health care reform, public safety and security, and the role of public inquiries. A less-than-buoyant fiscal surplus, escalating concerns about Liberal Party ethics and corruption, and a growing volatility in public opinion are examined, as are Canadians' increasingly uncertain views about the new leadership, particularly after a ten-year hold on power by the Liberal Party. Contributors include Frances Abele (Carleton University), Barbara Allen (University of Birmingham and Carleton University), Gerry Baier (University of British Columbia), Herman Bakvis (Dalhousie University), Gerry Boychuk (University of Waterloo), Douglas Brown (Queen's University), John Chenier (ARC Publications and the Lobby Monitor), Michael Dewing (Library of Parliament), Monica Gattinger (University of Ottawa), Geoffrey Hale (University of Lethbridge), Ian Hodges (Carleton University), Rachel Laforest (Queen's University), Russell Lapointe (Carleton University), Allan Maslove (Carleton University), Michael Prince (University of Victoria), Jack Stillborn (Library of Parliament), Christopher Stoney (Carleton University), and Reg Whitaker (University of Victoria).
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".