La plus ça change, la plus ça reste la même: An analysis of violence and conflict in Indigenous-Canadian government relations, and missing and murdered Indigenous women
Bibliographic record
Abstract
The aim of this thesis is to investigate the Canadian government’s stance on addressing the issue of missing and murdered Indigenous women, and to perform an analysis of ongoing violence and asymmetric conflict in Indigenous-Government of Canada relations. The Canadian government’s approach in regards to holding a national inquiry into the issue of missing and murdered Indigenous women in Canada was placed into the broader context of Indigenous peoples in Canada in order to understand the logic behind the government’s refusal and response. This was done by performing a deductive thematic analysis on gathered discourse and developed context, drawing the applied themes from the forms of violence outlined in Galtung’s violence triangle. As the results of the analysis indicated the clear presence of violence, both in the situation of missing and murdered Indigenous women as well as the broader context of relations, Galtung’s conflict triangle was employed to understand the significance of this violence. From this secondary analysis it was concluded that the issue of missing and murdered Indigenous women, as well as the government’s response, constitutes just one event in a much larger ongoing full conflict in Canada based on the underlying logic of settler colonialism and territorial gain.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.035 | 0.029 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".