Li zistwer Michif: aspects of narrative structure in Michif storytelling
Bibliographic record
Abstract
The linguistic reality of the Michif/Métis people is extremely rich and complex. Michif people have always been multilingual, or polyglots, to network with their kin and neighbors across the prairies. Their own ways of speaking have also emerged and today, there are languages unique only to the Michif/ Métis people in different areas of the homeland. The present socio-political status and the origin stories of some of these language varieties, referred to as mixed/heritage/southern/Turtle Mountain Michif, Michif French/Fransay and Northern Michif/Métis Cree (labels which exist only for the sake of distinguishing these language varieties from one another), are included in this study. This dissertation focuses on the structure of stories and narratives, and their role in language description and transmission from a few Michif communities across the homeland, as narrative structure in languages of the Michif/Métis has generally been understudied. In the case of mixed language Michif stories originating from the area of Ste. Madeleine (Manitoba), we delve into a componential analysis particularly focusing on the understudied use of complementizers in the conjunct verb inflection. Through a thorough qualitative analysis of complementizers in narratives recorded in fieldwork, we show that they have modal and aspectual values and as such play a crucial role in the cohesion and coherence of a narrative. Michif Fransay (Michif-French) has also been the subject of several linguistic studies, especially on its particularities from any other variety of Laurentian French. In this study, we focus more specifically on an interactional analysis of narratives in this language as spoken in St. Laurent and St. Eustache (Manitoba) and the role of the co-construction process in building a local historiography. A comprehensive study of group narratives shows how co-construction provides an internal perspective into each community’s history, worldview, people and language. Finally, working on narratives is a particularly personal process and it is crucial to center such work around the principles of Indigenous methodology and epistemology. At the heart of these protocols is building respectful relationships and this project focuses on how principles and protocols of Indigenous research methodologies and participatory action research can, and have, led this graduate-level project.
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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 | 0.002 | 0.005 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".