Bibliographic record
Abstract
Since the work of the Macdonald royal commission was concluded over 20 years ago, there have been few occasions for Canadians to engage in a comprehensive discussion of the country’s economic and social prospects and which policies we could pursue to improve them. Even in the 2006 federal election campaign, the policy debate focused on a few specific, short-term issues and offered little in the way of competing views on the major policy challenges facing Canada in the years ahead. The IRPP created the Canadian Priorities Agenda (CPA) to contribute to a broad-based and informed public debate on the economic and social policy choices and priorities for Canada.The central theme of the project is scarcity of resources and the need for choice: the everyday reality for policy-makers is that governments have lim-ited means at their disposal — be it revenue, manpower or political capital — and must therefore choose carefully which policies to pursue and which to leave behind.1 In making these choices, governments are understandably drawn to what is expedient and popular, but they should also consider the overall costs, benefits and distributional effects of various policies. Policies that offer genuine net benefits to society, even if
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 | 0.036 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.080 | 0.023 |
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".