Indigenous Encounters with Neoliberalism: Place, Women, and the Environment in Canada and Mexico
Bibliographic record
Abstract
The recognition of Indigenous rights and the management of land and resources have always been fraught with complex power relations and conflicting expressions of identity. In Indigenous Encounters with Neoliberalism, Isabel Altamirano-Jiménez explores how this issue is playing out in two countries very differently marked by neoliberalism's local expressions - Canada and Mexico. Weaving together four distinct case studies, two from each country - Nunavut, the Nisga'a, the Zapatista Caracoles in Chiapas, and the Zapotec from Juchitán - Altamirano-Jiménez presents insights from Indigenous feminism, critical geography, political economy, and post-colonial studies. These specific examples highlight Indigenous people's responses to neoliberalism in their respective countries, reflecting the tensions that result from how Indigenous identity, gender, and the environment have been connected. Indigenous women's perspectives are particularly illuminating as they articulate diverse aspirations and concerns within a wider political framework. What emerges is a theoretical and empirical discussion of how indigeneity as an act of articulation is embedded in tensions between local needs and global wants. By exploring Indigenous peoples' relations to and in different locations, this study attempts to uncover the complexities of materializing neoliberalism and the fluidity of indigeneity.--Résumé de l'éditeur
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".