“You Can Make a Place for it”: Remapping Urban First Nations Spaces of Identity
Bibliographic record
Abstract
Contemporary research on migration, particularly those studies drawing upon theories of transnationalism, demonstrates the ways in which social relations are stretched across spaces, allowing individuals to disrupt boundaries and create identities of belonging to more than one place. This research focuses on the disruption of state boundaries through migration and identity construction. In this paper we utilize elements of transnational theory and stories of First Nations migrants to explore the ways that First Nations urbanization also disrupts boundaries. Colonial perspectives and practices that confined First Nations cultural practices and identities within the physical boundaries of reserves and defined all other spaces as settler spaces created a framework for the construction of the contemporary Canadian nation-state. We present the results of eighteen in-depth interviews conducted with urban First Nations migrants. The interviews focused on understanding how migration to cities shapes relationships to the land (an important element of indigenous identity), the challenges cities present to maintaining connections to the land, and the strategies First Nations migrants use to preserve those connections. By resisting the assignment of First Nations cultures and identities to reserves, First Nations migration to cities challenges the identity of the modern state by disrupting its internal borders and boundaries.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".