Preface
Bibliographic record
Abstract
A s the principal investigator of the Ontario Looking After Children (OnLAC) Project since it began in the year 2000, following the award of a strategic research grant in late 1999 from the Social Sciences and Humanities Research Council of Canada (SSHRC), I aim to recount the history of the OnLAC Project, in some detail, in this preface.In Chapter 1, my co-authors and I provide further information about the Looking After Children approach, which has been an important contributor to the reform and improvement of child welfare practice, policy, and research in Canada and other countries.We hope that this volume enhances the role of Looking After Children in improving caregiver, staff, and student training, child welfare services, and young people's outcomes and well-being.In 1993, I travelled to Sudbury to attend a colloquium at Laurentian University.The invited speaker was Professor Roy Parker, from the Department of Social Policy and Social Planning at the University of Bristol, in England.He described a new approach that he and his colleagues were creating in the United Kingdom for assessing outcomes experienced by children and adolescents who were "in care," that is, being looked after away from home.The model considered by Professor Parker was known as "Looking After Children: Good Parenting, Good Outcomes."Based on the best child welfare science available at the time, it was being developed by an independent Working Party on Child Care Outcomes.(In the United
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.253 | 0.078 |
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".