Bibliographic record
Abstract
In Native North America, clinical/healing spaces are caught up in political struggles for autonomy. In Canada’s Northwest Territories, where rates of alcohol consumption are substantially higher than national averages, there are ongoing attempts to align therapeutic practice with traditional Aboriginal modes of healing and well-being. This Think Piece traces the ‘therapeutic trajectory’ of alcohol treatment in and out of this subarctic region. I show how the language of ‘evidence-based practice’ affords both gains and losses with regard to the assertion of collective identity and values vis-à-vis the state. Against the backdrop of the closure of the region’s sole residential treatment program, I contrast a conversation with a clinician responsible for implementing culture-based programs with the experiences of Destiny, a young Dene woman who, in the absence of local treatment options, spends time in clinics some one thousand kilometers away from her home community. In her movements away from the place to which she is indigenous, Destiny activates different forms of Aboriginal care than those intended by state and community actors. These divergent perspectives speak to the enmeshment of addiction with the perils and politics of liberal forms of recognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.147 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".