The Path Forward: A Road Map to Increasing Skills Development for Aboriginal People in Canada
Bibliographic record
Abstract
There is a projected decline in labour participation and productivity within Canada, this could be partially offset by increasing the participation rate and productivity of Aboriginal peoples in Canada. Aboriginal peoples in Canada face multiple barriers to education and employment; resulting in lower educational attainment and higher unemployment rates. Change It Up Trades (CIU) is a program designed to address these barriers and provide the support necessary for Aboriginal peoples to succeed at attaining an apprentice trade ticket. Change It Up is an eleven-month competency-based training program that uses in-community programming, alternative college entrance requirements, and a guaranteed industry apprenticeship. Through building partnerships with communities, businesses, and educational institutions CIU is able to address barriers to employment while also ensuring that the skills gained by participants are aligned with the labour market demands within their region. The program will need to complete a community assessment, economic analysis of regions, and secure funding from government bodies as well as private corporations in order to begin implementation. Change It Up will be implemented as a social enterprise, ensuring that after the first three years of funding the business purchased by CIU will generate enough profits to cover the costs of the training program, eliminating the need for further funding. Change It Up is a cost effective way to successfully addresses barriers to education and increase employment outcomes for Aboriginal peoples in Canada.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".