"Follow All the Rules:" Hunter and Angler Identity Formation, Group Boundaries, and the Ethno-Racialization of Poaching in Ontario
Bibliographic record
Abstract
Throughout Ontario’s hunting/fishing worlds, Asian Canadians and Indigenous people have been defined as excessive hunters/fishers and a threat to wildlife and to White Canadians' group interests. These images connect to longstanding racial-ethnic ideologies which have complicated intergroup relations and undergirded racial violence in hunting/fishing. Using semi-structured interviews with 55 White, Asian, and Black Canadian respondents and an analysis of online and print sources (e.g. social media, articles, websites), this thesis analyzes experiences with and responses to prejudice and discrimination, group boundaries, and identity formation processes in hunting/fishing. Group Position Theory, Colour-Blind Racism Theory, Stigma Management Theory, and Identity and Social Identity Theories were used to flesh out the collective meanings that inform hunter/angler identities and belief systems, including those that constitute ideological frameworks which distinguish between in and out group members along racial-ethnic lines. The results showed that identifying as a hunter/angler not only involves learning or reaffirming specific roles, responsibilities, and codes of behaviour based on environmental stewardship, but also racial-ethnic, anti-immigrant, and settler colonial (anti-treaty) ideologies that shape perceptions and behaviours towards ‘outgroups.’ These ideologies are primarily expressed through Colour-Blind Racism and define and ethno-racialize outgroups as poachers, as inferior, and as a group threat, particularly Indigenous people, Asian Canadians, and to a lesser extent, White Eastern Europeans. From this, feelings of group superiority can develop among White (especially Northern European) Canadians and prejudices can arise as a reactionary response to protect their outdoor privileges and identities. Additionally, men and women of Colour and White women who hunt/fish revealed various encounters with discrimination (e.g. suspicion, unsolicited education, racial/misogynistic comments) and how they engaged in numerous responses to confront or avoid racism and misogyny. Overall, this study demonstrates the unique ways social hierarchies unfold within hunting/fishing and how prejudices embody perceived threats to (predominantly White, male) settlers’ sense of group position and the status quo.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".