Ientsitewate’nikonhraié:ra’te Tsi Nonkwá:ti Ne Á:se Tahatikonhsontóntie
Bibliographic record
Abstract
This research has given voice to adult second-language speakers in Kahnawà:ke to help in identifying how they can be supported to continue on their language-learning journey to insure highly accurate unabridged language will be passed on to the next generations. Recognizing these adult second-language speakers as a high priority demographic is essential and timely, as many graduates of adult immersion combined with the first generations from elementary immersion now need the most support and motivation to raise their young families in the language. After years of hard work, patience and dedication, Kanien’kéha revitalization in Kahnawà:ke seems to be at a threshold: it seems as though the next steps in language revitalization will be pivotal. The research suggests the future entails taking a kincentric approach to community language planning and serves as the first study on the impact, successes and challenges of second language speakers in Kahnawà:ke.
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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 | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.028 |
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".