Indigeneity in Cities: Recognition, Misrecognition, and the Economic Stories of Indigenous Persons in British Columbia
Bibliographic record
Abstract
Approximately one million Indigenous persons in Canada live in cities, so understanding their experiences is vital to grasping contemporary Indigenous economic life. Yet research on Indigenous economic experiences focuses on how rural Indigenous nations in Canada engage with traditional practices such as hunting, fishing, and gathering. While this is valuable, these insights do not obviously apply to Indigenous economic experiences in cities. Instead, rural Indigenous communities, especially Indigenous reserve communities, have economies associated with their geographic space distinct from urban spaces. Indigenous economic experiences in rural and urban areas reflect a range of differences; for instance, living in a city tends to be more expensive than living in a rural town, while Indigenous rural reserves can provide cheaper housing for nation members and be sites of alternative give–giving economies. This article draws on interviews with diverse Indigenous persons living in urban settings on the West Coast to explore the ways that they negotiate their economic life in the city. Thick descriptions show that they navigate objectification and the misrecognition of their persons, among other concerns, by drawing on customary ideals from Indigenous communities and by recasting colonial economic forms, discourses and practices for their own purposes. Ultimately, this article is an invitation to take up the varied ways Indigenous persons navigate economic life in cities, telling their stories of economic relationships as they are and as they should be.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.020 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".