Bibliographic record
Abstract
The Learning Enrichment Foundation (LEF) in Toronto, Ontario (Canada) offers 12 targeted training programs that have successfully helped hard-to-serve clients return to the workforce. Compared with traditional training, targeted training has a much narrower focus and adapts quickly to industry trends to meet employers' changing demands. LEF's targeted training programs fall into the following general categories: industrial skills, early childhood assistant, and computer training. Since their inception, LEF's programs have developed and adapted in response to local employers' changing needs and feedback. In addition, LEF integrates self-marketing training through the targeted training courses. The self-marketing training is delivered through 4-day workshops that build on the hard skills developed in targeted training by focusing on the soft skills and motivation necessary for a successful job search. In 1998, LEF began an Ontario Works Demonstration during which 501 social assistance recipients were able to access LEF's assessment, counseling, training, and job search programs. Of the 501 participants, 363 (72%) received training and 138 (28%) were placed directly in the program's job search component. Within 2 years, 242 participants had moved into employment and 129 of the 158 training graduates moved into jobs that were directly related to their training and paid starting hourly wages of $6.85-$25. (MN) Reproductions supplied by EDRS are the best that can be made from the ori inal document. 2000 Targeted Training: An Integrated Initiative Joe Valvasori The Learning Enrichment Foundation Toronto, Ontario, Canada PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".