Bibliographic record
Abstract
The Mi'kmaw First Nation is one of the original Nations inhabiting what is now the Maritime Provinces of Canada. As a Mi'kmaw woman, I am aware of the discrimination, violence, and injustices that have continued throughout the history of my people from the time of European contact. In this thesis I argue that discrimination and injustices are based on racism, which has been normalized over the last several centuries. I focus on the Mi'kmaw perspective on racism and question the process that makes racism seem normal and acceptable in modern society. Drawing on postcolonial theory, I present a narrative of Mi'kmaw experience with colonialism, focusing on the effects of colonial practice on oral history, spirituality, and the traditional family structure. I argue that racism against the Mi'kmaq has become rooted in routine practices by dominant groups, making the behavior acceptable in our society. My argument is that racism is denied and not always acknowledged; therefore, it is infused and reproduced into everyday lives as something that is acceptable and normal. As a primary research method, I organized and participated in a sharing circle, which followed traditional Mi'kmaw custom. The process of the circle and the results are described in the thesis. I used autoethnography to facilitate the sharing circle from my own experiences and the life experiences of others to show that racism exists explicitly and implicitly, and uncover how it is reproduced and perpetuated. I find that, if we do not recognize and acknowledge racism, whether it is explicit or implicit, and work to dismantle it, racism and the oppression of First Nations will continue.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.053 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".