Social Exclusion, Structural Violence and the denial of Social Justice and Human Rights of Indigenous Communities: An Ethnographic Study of Whitefish River First Nation in Birch Island, Ontario, Canada
Bibliographic record
Abstract
While Canada is generally upheld as a multicultural and a tolerant society, this research demonstrates that social exclusion and discrimination continue to be the experience of First Nation communities in the country. It focuses on the experiences of the First Nations Communities in the metropolitan areas in Northern Ontario region in Canada, specifically in the Manitoulin Island Municipality in the 21st century in Whitefish River First Nation, Birch Island. It uses an ethnographic approach and the use of individual life narratives to reflect on how these legacies of exclusion and discrimination influence their lives as individuals and as a community, over a period of time and beginning from their early childhood. It demonstrates the continuing disadvantages and marginalisation, that these peoples face with regard to their basic capabilities, i.e. , income, health and education. In addition, it also highlights the effects of racism that still pervades their lives. The paper argues that these practices continued over a period of time have resulted in these communities experiencing physical, structural and cultural violence, it claims that these features demonstrate serious social injustice for these groups, reflected in a denial of their social citizenship and human rights.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".