Abenaki Language Revitilization in New England and Quebec
Bibliographic record
Abstract
Since the 1800s, European and American settlers have denied the existence of the Abenaki people in New England. Due to wars, intermarriage with Anglos, disease, mass migrations, and a eugenics movement of the 1920s in Vermont and New Hampshire, many people have assumed that the Abenaki are extinct. It is a testament to the Abenaki people that they have survived and thrived. One aspect to the Abenaki surviving as a people is through their language. This presentation will focus on both the Abenaki in Quebec and the Western Abenaki people in New Hampshire and Vermont, what steps they are taking to revive their language. With input from Abenakis in New England and utilizing current scholarly research, my presentation will focus on Abenaki language revitalization programs. I will also discuss how new generation gives hope to the Abenaki communities. Not only adults are learning the language, the children are learning it as well. Thanks to efforts from the Bruchac family—notably Joseph and his son Jesse—a completely new generation of Abenaki children are learning their language, and in some cases, they are speaking it as a first language. By reviving their language, they are forming new connections with one another, the land and with their ancestors.
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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 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".