The challenges of ethnic and racialized separation for truth and reconciliation in Canada
Bibliographic record
Abstract
In 1975, anthropologist Niels Braroe published Indian and White: Self Image and Interaction in a Canadian Plains Community. Braroe concluded that members of the Nekaneet First Nations (NFN), who resided in or made periodic trips to the neighboring Settler community of Maple Creek, Saskatchewan, operated on the fringe of economic and social activities, where they embattled the imposed social stigma of being ‘Indian’. Nearly 50 years after, we returned to Maple Creek to learn if conditions had changed since Braroe’s original research. We found that distinct categories of Indian and White were still very much intact, with identities generalized and fixed by pejorative beliefs. The intention in this research was not to uncover tensions that may exist between the NFN and Settlers. Rather, as applied anthropologists we sought to understand the conditions that foster the obstinacy of values and cultural behaviors that could impede meaningful reconciliation. As noted by Canada’s Truth and Reconciliation Commission, a necessary starting point for national healing involves coming to terms with the events of the past and gaining a shared recognition of the inequalities that continue to exist today.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.095 | 0.047 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".