DEVELOPING A COMMUNITY BASED MODEL TO IMPROVE LABOUR MARKET OUTCOMES FOR ABORIGINAL PEOPLE IN WINNIPEG
Bibliographic record
Abstract
The Province of Manitoba has among the highest percentage of Aboriginal people in Canada who continue to measure poorly on several social and economic indicators when compared with non-Aboriginal people. It is a much younger population and growing very quickly. While the Aboriginal population in Manitoba grows, their participation in the labour market continues to lag far behind that of the non-Aboriginal population. The majority will access education and employment without great difficulty— highly educated and skilled Aboriginal people are in very high demand. However, the legacy of colonization and continued systemic racism has left a host of barriers to reaching their full potential. Improving labour market outcomes will require that we rethink existing interventions and close existing gaps. There are many organizations providing training opportunities for Aboriginal people wanting to enter the labour market, however supports beyond training are minimal. Successfully transitioning trainees into employment has been described by one Indigenous educator as “the next pressing issue” that policy makers, educators, trainers and communities must grapple with. This article describes the gaps that have been identified and explores a solution proposed by those who provide training to Aboriginal people and the people who aim to hire them. Why have governments been hesitant to take the necessary steps to address this critical gap and how might this be resolved?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 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) | 0.000 | 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 teacher head, 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".