Bibliographic record
Abstract
Michif is an endangered language spoken by approximately a few hundred Métis people, mostly located in Manitoba and Saskatchewan, Canada. Michif is usually categorized as a mixed language (Bakker 1997; Thomason 2003), due to the inability to trace it back to a single language family, with the majority of verbal elements coming from Plains Cree (Algonquian) and the majority of nominal elements coming from French (Indo-European). This book investigates Bakker’s (1997) often cited claim that the morphology of each source language is not reduced, with the language combining full French noun phrase grammar and Plains Cree verbal grammar. The book focuses on the syntax and semantics of the French-source noun phrase. While Michif has features that are obviously due to heavy contact with French (two mass/count systems, two plural markers, two gender systems), the Michif noun phrase mainly behaves like an Algonquian noun phrase. Even some of the French morphosyntax that it borrowed is used to Algonquianize non-Algonquian borrowings: the French-derived articles are only required on non-Algonquian nouns, and are used to make non-Algonquian borrowings visible to the Algonquian syntax. Michif is thus shown to be best characterized as an Algonquian language, with heavy French borrowing. With such a quintessentially ‘mixed’ language shown to essentially not mix grammars, the usefulness of this category for analysing synchronic patterns is questioned, much in the same way that scholars such as DeGraff (2000, 2003, 2005) and Mufwene (1986, 2001, 2008, 2015) question the usefulness of the creole language classification.
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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 | 0.000 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".