Bibliographic record
Abstract
This thesis investigates the negative particles used in two varieties of Cree: Muskeg Cree (a.k.a. Swampy Cree) and Plains Cree. Previous literature on Plains Cree has implicated a variety of factors potentially relevant to the choice of negative particle, including: inflectional order, clause type, clausal versus constituent negation, reality status, and veridicality (Cook 2014; Reinholtz and Wolfart 1996; Déchaine and Wolfart 2017). Discussion of Muskeg Cree has broadly claimed that inflectional order alone determines the negative particle used (Ellis 2000; MacKenzie 1992). This thesis investigates these attested patterns of negation in Plains Cree and Muskeg Cree, using a variety of methods from variationist sociolinguistics, statistical analyses, and semantic fieldwork. Corpus analysis of published literary materials was performed, as well as interviews with speakers that included translation, grammaticality judgment tasks, and storyboards. The results indicate that Plains Cree negation broadly reinforce the claim that inflectional order and reality status are relevant factors affecting negator use. It also appears that there may be additional unidentified factors at play in negation in this variety. In contrast, Muskeg Cree negation does not appear to be consistent with the previously identified pattern. The negator mwāc, previously considered marginal, is the overwhelmingly preferred negator for non-imperative negation across the modern speakers interviewed. Mwāc is used across a variety of contexts, regardless of inflectional order, while other non-imperative negators, ēkā and mōna, are dispreferred, rare, or even non-existent for many speakers. Substantial variation in negator use was observed for Muskeg Cree, both between literary and elicited materials, as well as between speakers being interviewed. This indicates that the hetereogeneity within the Muskeg variety of Cree has been underestimated, with greater variation in how speakers are able to express themselves through negation. These findings have implications for the teaching of negation in Muskeg Cree, and support the use of varied methodologies in the examination of language. They provide new insights into the pattern of use of negation in Muskeg Cree, an understudied language.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".