Percentage of Indigenous and non-Indigenous adults (15 years and older) having completed tertiary education (Bachelor's and above), most-populated Canadian metropolitan areas, 2016
Bibliographic record
Abstract
Across many OECD countries, Indigenous People represent an important and growing demographic group with a unique set of cultures and customs.In a local development context, many Indigenous People are within remote areas and face unique challenges in finding quality employment and economic development opportunities.They often experience lower outcomes than the non-Indigenous population on a number of key economic and social indicators.Well-designed employment and skills policies are fundamental to link Indigenous People to high quality jobs, while also contributing to broader economic development objectives and inclusive growth.The OECD LEED Programme has built a large body of evidence on "what works" for disadvantaged groups, including Indigenous populations over the past 35 years.This work has demonstrated the importance of providing more autonomy to the local level to enable policy innovation.With regard to Indigenous People, this is critical in supporting the principle of self-determination.In consideration of the Truth and Reconciliation Commission of Canada's Calls to Action, this report is set within the context of furthering the restoration of Indigenous rights at the national, regional and local level.To do so, this report analyses employment, education, job creation and local development outcomes of Indigenous People within Canada.After determining the barriers preventing Indigenous People from reaching better outcomes, the report proposes potential ways forward for Indigenous labour market and skills programming.With this report, the OECD hopes to place Indigenous voices at the forefront of the discussion and looks to Indigenous communities as the leaders of local, regional and national change.I would like to warmly thank Employment and Social Development Canada for their active participation and support of the study, and for their ongoing partnership with the OECD.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".