Fantastic topographies: neo‐liberal responses to Aboriginal land claims in British Columbia
Bibliographic record
Abstract
This paper presents an analysis of the referendum on Native land claims that took place in British Columbia (BC) in the spring of 2002. The province's Liberal Government claimed that the referendum was needed in order to secure a public mandate for a set of negotiating principles that would breath new life into the supposedly stalled treaty process. Drawing evidence from government press releases, politicians' statements and media coverage, we argue that the BC Government and its supporters employed a discourse centred on neo‐liberal economic logic in order to justify the exercise. Furthermore, we charge that this discourse relies on an erasure of the historical–geographical contexts of Native–newcomer relations in the province. By drawing on Cindi Katz's socio‐spatial metaphor of ‘topographies’, we suggest that Native space in British Columbia needs to be understood as a series of situated and grounded experiences of colonialism and capitalist production. Then, extending the metaphor, we highlight the ways in which the referendum supporters' rhetoric contains a vision of future topographies of Native experience that adhere to the private property ethic of neo‐liberal economics. We conclude that the politics surrounding the treaty process must be understood as a contest over the terms of Aboriginal citizenship and not merely as a conflict over the allotment of land and resources.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.031 | 0.021 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".