Developmental Assets and Tutoring
Bibliographic record
Abstract
We present selected findings from the long-term Ontario Looking After Children (OnLAC) project (2000–present). From the beginning, the project has aimed to contribute to improved child welfare outcomes and policies in Ontario through the annual assessment of service needs and the monitoring of developmental outcomes among children and youths in out-of-home care in the province. To date, the project has conducted some 80,000 interviews that have included an estimated 35,000 young people, aged 018+ years, their caregivers, and their child welfare workers. We first describe the origins of the OnLAC project in reform-oriented research and policy innovation in the UK. We then discuss the special role that developmental assets have come to play in the action theory of the OnLAC model, based on extensive research by the Search Institute in the US and our own OnLAC results. Next, we present findings on the educational outcomes of Ontario children and youths in care of preschool, primary school, secondary school, and post-secondary ages. There is clearly a need for improved results at all educational levels, through recourse to educational interventions of demonstrated effectiveness. After a brief survey of US and UK sources of evidence-based interventions, we take up the evidence in favour of tutoring, which we believe offers untapped potential for enhancing the educational success of young people in care. We summarize the experimental evidence on the positive effects of tutoring and present the promising results of several randomized trials in Ontario that we and colleagues have conducted of tutoring with children in care. We end by mentioning the improved educational outcomes that are now possible with the advent of high-impact tutoring, which is being actively promoted by https://www.w3.org/1999/xlink" xlink:href=" https://ProvenTutoring.org ">ProvenTutoring.org , a recently launched coalition of field-based organizations in collaboration with university researchers in the US.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".