Bibliographic record
Abstract
The provision of child care services in cities presents an opportunity to examine the spatial dimensions of a social service that is provided both publicly and privately. Geographical access to a service must be regarded as an important component of the effectiveness of that service. The problem of unequal access to a social service such as child care is important for both policy makers and researchers-and, obviously, for the consumers involved. There are strong links between the locational and political issues involved in child care. Geographical analysis can contribute to an understanding of the effects of present and proposed policies on child care, particularly their spatial and redistributive impacts. For instance, Canada’s mixture of ownership types-municipal, non-profit and commercial-has interesting implications for service provision and access, especially as non-profit and commercial centers often have quite different spatial distributions within cities. Thus, the actions of government and non-government agencies are an important set of constraints under which parents’ choice of child care center is made, even when such actions are intended to broaden choice. Access to child care includes the institutional factors such as regulations and policies that make up the criteria for eligibility for admission to the child care system, the level of government funding, the prices charged, and waiting lists (priorities) for child care spaces (see Truelove, Chapter 3, this volume). Government agencies have had major impacts on the availability of child care, and especially on the availability of subsidies (one of the greatest difficulties for many families is that few new subsidized spaces are being created). Changes in policies can create major changes in the supply and demand for child care. Thus the diffusion of child care centers is dependent on the agencies and providers and their creation of the service. The role of institutions in the provision of child care in Metropolitan Toronto is one focus of this chapter.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".