Bringing Cities to the Table: Child Care and Intergovernmental Relations. Ottawa: CPRN Discussion Paper F|26. Ottawa: Canadian Policy Research Networks. See http://www.cprn.org
Bibliographic record
Abstract
The growing interest and concern about Canada’s urban areas has provoked a recent wave of research papers and policy conferences in and about our cities. CPRN has chosen to contribute to this lively discourse by playing an integrative role – linking research and policy communities across Canada, across jurisdictions, and across disciplines. The first paper in the CPRN series by Neil Bradford (Why Cities Matter: Policy Research Perspectives for Canada, June 2002) reviewed a wide range of literatures to conclude that policy and government challenges are both vertical and horizontal. They are vertical because city-regions are strongly influenced by municipal, provincial, and federal governments, as well as international institutions. They are horizontal because it is important to link city-region networks from inner city to suburbs to rural hinterland. This paper continues the series, providing an applied perspective. By examining the experience with the design and delivery of child care services, the paper draws on both the Canadian situation and findings from other countries. The policy puzzle is how to ensure a measure of equity in access and quality of service across the broad population,
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".