Translating land: cultural development encounters in Canadian and Alaska Indigenous land studies, 1968–1981
Bibliographic record
Abstract
The 1970s saw an increase in the planning of oil infrastructures across lands in the American North claimed by Indigenous groups. As a result of this, federal pipeline inquiries in Alaska and Canada turned into testbeds for the legal and anthropological study of Indigenous land claims, their evidentiary regimes of land use, and legal and cultural definitions of Indigeneity. Studies of northern Indigenous subsistence in Alaska followed economic definitions of Indigeneity embedded in development discourses of modernization. In contrast, the anthropological research produced in Canada by state-backed anthropologists in partnership with Indigenous groups between 1973 and 1981 embraced Indigeneity and its land use practices as cultural systems in need of translation and monetization. This article studies the resulting graphic, bureaucratic, and methodological artifacts of Canadian land use studies to reveal the structural components that enabled anthropological mappings to be used as evidence in development inquiries. By employing frameworks of semiology and cultural anthropology, the Canadian mapping practices strengthened the position of anthropologists as ‘translators’ and ‘interpreters’ between planners and their subjects. The article posits that the shift from spatial to comprehensive development planning made land use the basis of a narrow cultural, economic, and legal definition of indigeneity.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.056 | 0.027 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".