Beyond Blood: Rethinking Aboriginal Identity and Belonging
Bibliographic record
Abstract
The traditional Aboriginal Nations in Canada, like the Mi'kmaq, Mohawk, or Maliseet, have been divided into multiple Indian bands. Their vast traditional territories have been taken up for settlement and the little land that remains in their possession is concentrated in tiny reserves. Similarly, traditional Aboriginal identities have been divided into so many legal and political units, that even families can be divided along these same lines. Many Aboriginal people now identify as status and non-status Indians, with further sub-categories like 6(1) and 6(2) status Indians, or original members and restored members. Single communities can be bitterly divided along these lines and attempts of reasserting traditional identities often incorporate these same divisions. This thesis looks at the long history of government interference with the identities of Aboriginal peoples and their communities and how that continued interference has resulted in divided communities, lengthy litigation and bitter politics. While Canada has officially rejected assimilation as a goal, the antiquated 'Indian Act' still imposes Indian identities upon Aboriginal peoples and excludes large groups of Aboriginal people. This issue is legally, culturally, and politically sensitive and solutions are often seen as too controversial to effect much-needed change. My contributions to this issue can be categorised into four main arguments: (1) the preservation of Aboriginal culture identity is not only a worthwhile endeavour, but is a necessary part of ensuring that Aboriginal peoples have access to the good life, (2) Aboriginal Nations have the right to determine their own identities (citizenship criteria), however this right is limited by the rights of others, including the right of individual Aboriginal people to belong, (3) the exclusion of some individuals, like non-status Indians for example, is currently based on discriminatory characteristics like blood quantum/descent, which do not reflect cultural identity, and (4) if band membership and self-government citizenship codes are based on these same discriminatory characteristics, then neither offers solutions for the future, but merely perpetuate the "status" quo. The solutions that I offer are based on the reinforcement of relationships between individuals, families, communities, and Nations, versus the sole reliance on singular identity markers.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.033 | 0.094 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".