Implementing the Right of People with Disabilities to Vocational Training. Report of an Action-Research Seminar, Quebec City, 25-26 Aug. 2008
Bibliographic record
Abstract
The Action-Research seminar was co-organized by the ILO Skills and Employability Department in collaboration with the CTNERHI (Centre technique national d'?tudes et de recherches sur les handicaps et les inadaptations), Global Applied Disability Research and Information Network on Employment and Training (GLADNET) and the Rehabilitation International Work and Employment Commission, with funding support by the Government of Ireland and Rehabilitation International (RI). A follow-up to the Tripartite European Regional Meeting, "The Inclusion of Persons with Disabilities in Vocational Training and Employment", held in Geneva, March 2007, and to an Expert Group Meeting on the same topic, held in Bangkok, February 2006, the seminar had three main objectives: 1. to identify elements of good practice in skills development for people with disabilities in countries around the world; 2. to highlight areas in which more progress is needed and examine attempts to address these challenges; 3. to formulate an agenda for action and research. The seminar took place in the form of four optional sessions during the Rehabilitation International World Congress, "Disability Rights and Social Participation: Ensuring a Society for All". Participants included skills development practitioners, government officials including policy-makers and administrators; disability advocates and researchers.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".