Bibliographic record
Abstract
In Aboriginal™ , Jennifer Adese explores the origins, meaning, and usage of the term “Aboriginal” and its displacement by the word “Indigenous.” In the Constitution Act, 1982, the term’s express purpose was to speak to specific “aboriginal rights”. Yet in the wake of the Constitution’s passage, Aboriginal, in its capitalized form, became increasingly used to describe and categorize people. More than simple legal and political vernacular, the term Aboriginal (capitalized or not) has had real-world consequences for the people it defined. Aboriginal™ argues the term was a tool used to advance Canada’s cultural and economic assimilatory agenda throughout the 1980s until the mid-2010s. Moreover, Adese illuminates how the word engenders a kind of “Aboriginalized multicultural” brand easily reduced to and exported as a nation brand, economic brand, and place brand—at odds with the diversity and complexity of Indigenous peoples and communities. In her multi-disciplinary research, Adese examines the discursive spaces and concrete sites where Aboriginality features prominently: the Constitution Act, 1982; the 2010 Vancouver Olympics; the “Aboriginal tourism industry”; and the Vancouver International Airport. Reflecting on the term’s abrupt exit from public discourse and the recent turn toward Indigenous, Indigeneity, and Indigenization, Aboriginal™ offers insight into Indigenous-Canada relations, reconciliation efforts, and current discussions of Indigenous identity, authenticity, and agency.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.043 |
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".