Bibliographic record
Abstract
It is now widely acknowledged that Canada’s cities need help if they are to reach their economic potential and offer a high quality of life to their citizens. Indeed, there is growing evidence that social and economic conditions have deteriorated for many urban citizens, the most vulnerable being single-parent families, Aboriginal people, recent immigrants, visible minorities, elderly women, and the disabled. Major questions remain as to what kind of help the cities need and from whom. And here much attention has turned to the federal government, even though the constitution says that municipalities are the “creatures ” of the provinces, and most provinces guard this role jealously. To help clarify the potential roles for Ottawa, CPRN commissioned four papers. The first four focus on urban poverty, immigration, Aboriginal people, and housing. A fifth provides an overview of the ideas in the first four papers, and includes the reflections of a diverse group of Canadians from many sectors who participated in a Roundtable. Each of the papers provides a summary of the state of knowledge in their area and then sets out possible actions for the federal government. All four papers point to the challenges of governance of our cities. And, despite the
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.042 | 0.012 |
| Scholarly communication | 0.012 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".