Levels of Developmental Assets and Educational Outcomes in Young People in Transitional Living in Canada
Bibliographic record
Abstract
"Developmental assets may be defined as significant relationships, skills, \nopportunities or values that protect young people in the presence of risk and \npromote their resilience. The purpose of this study was to discover whether \nhigh, medium, and low levels of developmental assets among transition-age \nyoung people in care were related to selected educational outcomes. If so, \nchild welfare staff could potentially use their knowledge of a youth's level \nof assets to plan an appropriate level of educational assistance that would \nenable the youth to be more successful in his or her transition. The sample \nwas composed of 567 young people (322 females and 245 males), aged 18-20 years, who were residing in a transitional living program in Ontario, \nCanada. The three levels of developmental assets were found to have statistically \nsignificant relationships with the seven educational outcomes examined \nthat ranged between small-to-medium and strong in size. The educational \noutcomes consisted of the educational level in which the youth was \ncurrently enrolled, the highest educational level attained, average marks in \nschool, participation in volunteering, employment, education or training, \ndevelopment of skills useful for employment, and adequacy of planning for \nthe youth's education. The implications of the findings for rendering educational \nassistance to youths in particular need were discussed." (author's abstract)
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".