Bibliographic record
Abstract
Heavy drinking has been a feature of the village lives of the Innu people of Labrador ever since they were coerced to abandon permanent nomadic hunting in the 1950s and 1960s, when the government-built villages of Sheshatshiu and Davis Inlet (or Utshimassits) were created. The process of sedentarization has accompanied a removal of the people from the hunting life in the interior of Labrador (known as the country or nutshimit), incurring a serious loss of meaning, purpose and autonomy. To combat heavy drinking, the Canadian authorities have imported into the Innu villages both pan-Native healing organizations and their own social services and criminal justice institutions. The Innu, through their political body, the Innu Nation, have also developed Healing Services. In these reflections, which are derived from my work with the Innu since 1994, I examine various approaches to healing and look at the experiences of some Innu with drinking. Paradoxically, although drinking is very often destructive, it can also be a form of emotional sharing, protest against assimilation and power to drinkers.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.042 | 0.051 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".