Closing the gap between what we know and what we do for Canada’s young children
Bibliographic record
Abstract
The impact of the early years on a child’s chances at success later in life is indisputable. Thanks to advanced understanding of the relationship between early experience, brain development and outcomes, we now know that the first years of life offer a unique opportunity, both for individual children and families, and for our society (1). Neuroscience has shown us that children’s early experiences are critical to future health, learning and behaviour. What happens to children during this time can set them on a lifelong course – for better or for worse. The Globe and Mail recently reported that 25% of all health care costs are devoted to caring for patients in their final year of life (2). End-of-life care is an indisputable marker of a compassionate society, but what do we give up to pay for this? Do we inject the same amount of economic energy into the first years of life?
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".