Bibliographic record
Abstract
The purpose of this study is to compare (a) the apprenticeship and secondary school records of those people in B.C. who started an apprenticeship while enrolled in secondary school (focus group) to (b) those who began their apprenticeship after leaving high school (comparison group). A total of 22,909 apprentices have had their Industry Training Authority apprenticeship and Ministry of Education grade 11 and 12 education records examined. The 13,357 individuals in the focus group began an apprenticeship while enrolled in secondary school and the 9,552 individuals in the comparison group started an apprenticeship after leaving secondary school. From these groups, individuals from five popular trades were selected for analysis to check for differences or similarities between these trades and demographic groups. Provincial apprenticeship and education statistics were used, where appropriate, to compare focus and comparison group findings to provincial norms. This research has found evidence suggesting that apprenticeship programming in secondary school encourages a wider variety of people to take apprenticeships. Apprenticeship training and associated programming in the grade 11 and 12 years appears to help increase student achievement levels and lead to significantly higher graduation rates for aboriginal and special needs students who can be marginalized in mainstream academic programming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".