Bibliographic record
Abstract
One hundred and fifty years ago there were some thirty Indian languages in British Columbia — each of them a rich repository of the history of the land and its people. Today all but two or three of the languages are still spoken. The survival of these languages is a tremendous statement of the cultural resilience of the Indian people in the face of repression, neglect, and misguided paternalism. For most of the languages, however, the immediate future is bleak. New events and changing circumstances combine to pose a more insidious threat to native language survival than ever existed previously. Of the languages still in existence, the majority are spoken by fewer* than 500 people, and in most of these the youngest native speakers are more than forty years old. The forty-year age mark is an especially telling and tragic one, for it indicates that there are no people of childbearing age who can perpetuate the language in the only fully effective way — by raising children from infancy in the language. Eight of the languages are spoken today by 500 or more people : Carrier, 2,000-3,000; Nishga, 2,000; Chilcotin, 1,500-1,700; Babine, 1,1001,500; Kwak'wala, the language of the Kwagiud, 1,000; Coast Tsimshian, 800; Halkomelem, the language of the mainland Coast Salish, 500; and Shuswap, 500. In only three of the languages, however — Carrier, Chilcotin and Babine — are children in any numbers learning the native language from infancy. The present is plainly a time of crisis for the maintenance, let alone the revival, of Indian languages in British Columbia. The question Why maintain the languages? might well be asked. For Indian people the answer is obviously one of individual self-identity and cultural continuity. To point to this answer is not to suggest that Indian people will cease to be Indians should their language disappear, but it is to indicate that language is the crucial component of culture.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".