Speaking the land: exploring women's historical geographies in Northern Québec
Bibliographic record
Abstract
This review article explores the significance of studying the historical geographies of Aboriginal women in Northern Québec and presents potential research avenues. The article's premise is that we cannot understand the historic and contemporary geographies of subsistence economies without more research about the roles that women played in them. Related to this issue is a broader reflection on how geographies of the past are reconstructed by historical geographers, both from an epistemological and methodological point of view. As a discipline, historical geography has been chiefly dedicated to the study of the encounter of migrant Europeans with new world lands and societies, with the result that Aboriginal and women's geographies have commanded less attention. This gap in knowledge should be addressed by emerging researchers. Taking an interdisciplinary approach and moving from the general to the particular, my inquiry evolves in three parts. First, I identify some key debates that are pertinent to a study of gender and Aboriginal women in a colonial context. Second, I review the existing ethnographic literature on Cree women in Eastern Canada and assess insights about their role in subsistence economies. Third, I outline specific avenues that help frame a research programme to study the historical geographies—by which I understand the places, placing, and place‐making—of Aboriginal women in Northern Québec.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".