The Importance of Accuracy in the Use of Grammatical Terms and Concepts in the Description of the Distinctive Properties of Plains Algonquian Languages
Bibliographic record
Abstract
The subject of this paper was inspired by my collaboration on a project involving the long-term histories of grammatical traditions led by Dr. Philomen Probert at the University of Oxford. Owing to my interest in linguistic typology and the study of the syntax-semantics-pragmatics interface in a number of languages, – especially Native American languages, which differ in many respects from Indo-European languages, –, I have observed that some languages cannot be accurately described if we use the grammatical terms and concepts commonly applied to the analysis of extensively studied languages such as English, Spanish or French, as certain grammatical properties of one language may not be equivalent to those of another and, consequently, require a different treatment. Thus, firstly, by adopting a holistic comparative perspective deriving from all areas of grammar, I aim to reveal the distinctive features that Plains Algonquian languages such as Cheyenne / Tsėhésenėstsestȯtse (Montana and Oklahoma, USA), Blackfoot / Siksiká, Kainai, and Pikani, (Montana, USA; Alberta, Canada), Arapaho / Hinóno´eitíít (Wyoming and Oklahoma, USA), and Gros Ventre / White Clay or Atsina / Aaniiih (Montana, USA) display when compared with Indo-European languages such as English, Spanish, French or German. The subsequent examination of these data will provide examples of terms and concepts that are typically used in traditional grammatical descriptions, but that do not serve to characterize the grammar of these Native American languages accurately. Finally, I will attempt to propose alternative terms and concepts that might describe the distinctive grammatical properties exhibited by these languages more adequately.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".