Changing behaviour patterns of disadvantaged unemployed youth through an intervention strategy using computer-based training techniques
Bibliographic record
Abstract
This thesis is concerned with changing behaviour patterns of a number of unemployed disadvantaged youth associated with an intervention strategy called Career Start. Career Start uses computer-based technology in its instructional program, which attempts to address simultaneously three significant barriers to youth employment. These barriers are grade level attainment, low self-esteem, and destructive attitudes towards work. The participants in Career Start numbered 169 youths between the ages of 15 and 24. All had been classified as severely employment disadvantaged by the classification methods of Employment and Immigration Canada. The youths were interviewed to ascertain specific background characteristics and the reasons why they were unemployed. They were also tested before and after participation in the program to determine whether or not barriers to employment had been reduced. There was some evidence of improvement in academic scores, primarily in the language and mathematics areas. In addition, there was a reduction in the barriers to employment as measured by the Vocational Opinion Index. The Culture Free Self-Esteem Index also showed that the self-esteem of the participants was raised after participation in the program. The employment objectives of Employment and Immigration Canada were also taken into consideration during the study. During the first year the objectives were exceeded by 36 percent of the subjects and, during the first nine-month period of the second year, the objectives were again exceeded. Data were statistically analyzed using the Chi-square Test for Independence, the Non Parametric Sign Test, cross-tabulations, and frequency tables. The study indicated that Career Start had the potential to be a successful intervention strategy in addressing the problems of unemployed and disadvantaged youth. The research also generated a number of related issues in need of further study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".