Re: SOLUTIONS -- How the Ontario government can rescue and ensure the viability and quality of the province's child care system
Bibliographic record
Abstract
Child Care is an essential part of an early learning and care agenda. It is a vital service for thousands of families in Ontario and an important contributing support to our economy. Child Care faces an unprecedented ‘crisis’ – we do not use this term lightly as indeed we are facing a dramatic reduction of child care services in Ontario. This open paper outlines the crisis, but more importantly, starts a dialogue toward solutions. SOLUTIONS offers a practical way forward. A way that can lead child care away from the coming collapse. A way that can lead to and foster a new healthy construct for this service. We have offered short term proposed actions that we recommend implementing in 2012 and a long term blueprint, based on evidence and best practice, which would modernize child care. This paper calls for increased public investment. As responsible Ontarians, we are acutely aware of the economic situation facing our Province. While we recognize this reality, we outline the positive contributions child care provides to our economy and remind that it is a key foundation for bringing people into the workforce. We cannot imagine a ‘modern’ Ontario without a base child care service. Indeed, while the pressures on the public purse are extraordinary, can we afford not to make this investment? On behalf of the Quality Early Learning Network, we welcome and hope that this paper stimulates discussion and reaction. We welcome your reaction by way of an email response to QELNetwork@gmail.com .
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".