Autonomy and minority groups : Different notions of autonomy and First Nations people
Bibliographic record
Abstract
As a result of colonialism, many First Nations minorities have to a large extent lost their autonomy. They wish to be self-determined, but as a consequence of oppression, First Nations people have a deeply rooted mistrust of the state. This has made it more complex for the society to support autonomy for these groups. In this thesis my aim is to investigate how society can support autonomy for First Nations people - which notion(s) of autonomy is/are more applicable. As autonomy often refers to the liberal notion of autonomy with individualistic implications, some argue that autonomy is not applicable to groups. Therefore, I also explore the possibility whether the relational notion of autonomy is more relevant for supporting First Nations people autonomy. Even though I find liberal notion helpful for many minority groups, the relational notion has more applicable criteria in order to support autonomy for most minority groups as they define themselves through relations more than having individualistic interests. This is shown to be especially true for First Nations people. I have found that what distinguishes First Nations people from other minority groups is their connection to their land which can be explained through their spiritual notion of sovereignty. This profound connection defines who they are and the way they live. Therefore, I conclude that the relational notion is not sufficient to support autonomy for First Nations People, their spiritual connection with their land should also be considered.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".