Are Canadian Indigenous Peoples Facing Marginalisation and Socio-Economic Disparities in 2024?
Bibliographic record
Abstract
This research examines the marginalisation and socio-economic factors faced by Canadian Indigenous Peoples. There was a focus on the interconnected challenges in housing, land access, health care, education, financial security, political and public policy representations. To address this, a multi-method approach was undertaken, combining qualitative research with statistical analysis of national and specific regions’ data. Policy documents were reviewed to assess the extent of their priority with Indigenous causes. Attention was given to variations across regions, especially for those situated in the Yukon, Nunavut and Northwest Territories.While colonialism officially ended generations ago, impacts nonetheless continue to prevail. The Canadian government has made numerous public commitments to reconciliation, yet meaningful systemic change is yet to happen. Socio-economic barriers persist as a broader failure to eradicate colonial systems and to empower Indigenous governance through possibly self-governing bodies. Despite continued policy commitments, many communities continue to face unequal access to essential services as the marginalisation of Indigenous Peoples in Canada remains a deeply ingrained issue that has been shaped by generations of colonial influences as well as systemic neglect. This project’s analysis emphasises the need for deeply required socio-economic reform and battling marginalisation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".