Algonquian morphosyntax: Toward a new descriptive model from innu-aimun data (Quebec)
Bibliographic record
Abstract
The morphosyntax of Algonquian languages, spoken in North America, is little known in Europe. The way they are traditionally described remains inherited from the first descriptions, dating back to the end of the 16th Century. These languages appear even more ‘exotic’ as the terminology used is idiosyncratic: “direct-inverse” system, “obviation”, “person hierarchy”, etc. This makes it seem like we are faced with linguistic facts never attested anywhere else. Hence, our subject is at once a descriptive and analytic one through our study of innu-aimun morphosyntax, an Algonquian language spoken in Quebec , and a subject of linguistic history and epistemology.Our analytic model is based on the immediate constituent analysis, which uncovers the structural logic specific to the innu language, where semantic factors interact with syntax at different constituency levels. This model places diathesis at the center of the exceptionally rich and complex Algonquian morphosyntax, which entails diathesis, voice and alignment phenomena, notably through different strategies of verbal argument making depending on argument semantics and referential values. Our study aims at contributing to general linguistic improvements, by bringing a new light to the complexity of Algonquian languages. We hope that the model we propose, quite divergent from Algonquian linguistic tradition but aligned with more recent works, would enable, through a new, global and comprehensive approach, to solve some aporia and to fill some gaps present in the descriptions of Algonquian languages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".