Let's put a national child care strategy back on the agenda
Bibliographic record
Abstract
The impact of the early years on a child's chances for success later in life is indisputable. Thanks to advanced understanding of the relationship among early experiences, brain development and outcomes, we now know what a unique opportunity these special years can provide (1). Evidence continues to demonstrate that the quality, type and availability of child care significantly contribute to the predictions of children's development (2). As many have argued, this reality is important not just for parents, teachers and paediatricians – and the many others who care for and work with children – but it is also critical knowledge for the future of our nation (3). Investments in quality early childhood care and education have the potential for massive returns down the road. So why does Canada rank so low among developed countries when it comes to overall spending on families and children, and specifically on early childhood care and education services (4)?
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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.047 | 0.030 |
| Insufficient payload (model declined to judge) | 0.050 | 0.013 |
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".