Bibliographic record
Abstract
Cet article traite des ethnonymes employés par les Abénakis, à travers le temps, pour désigner les nations autochtones les côtoyant. Comme bien des toponymes, les ethnonymes ont subi différentes variations à travers le temps, variations dues entre autres à la méconnaissance des langues autochtones et à la bureaucratisation de ce vocabulaire, qui rendent de nos jours difficiles leur lecture, leur compréhension et leur interprétation. À travers une recherche exhaustive des ethnonymes disponibles dans les sources primaires, il a été possible d’identifier dix-neuf ethnonymes se rapportant à quatorze groupes autochtones du Nord-Est (Algonquin, Attikamek, Huron-Wendat, Inuit, Iroquois, Malécite, Micmac, Passamaquoddy, Mohawk, Mohican, Naskapi, Nipissing, Odawa et Pénobscot). Plusieurs des ethnonymes trouvés se sont avérés être des emprunts linguistiques à d’autres langues algonquiennes, et certains groupes sont identifiés par plus d’un ethnonyme. Assez étrangement, certains des groupes du Québec n’ont pas de nom en abénakis malgré leurs contacts attestés à travers le temps.
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 | 0.001 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".