Bibliographic record
Abstract
This research aims to better understand the link between prosody and verbs in Anishinaabemowin by investigating pitch placement in relation to verb placement in Anishinaabemowin utterances. The data is from a story by Ogimaawigwaebiik archived in Dibaajimowinaan; Anishinaabe Stories of Culture and Respect. Anishinaabemowin, also known as Ojibwe, is a member of the Algonquin language family and is spoken throughout Southern Ontario and the Northern United States (Fairbanks, 2017). It is a polysynthetic language meaning it primarily uses affixes to convey meaning, particularly on the verbs. Prosody is the organization of various linguistics units (words, pitch, tone) into an utterance in the process of speech production. It conveys not only linguistics information but also contextual cues, intentions and attitudes (Fujisaki, 1997). This research utilized two audio softwares, Audacity and Praat, to clean and segment the audio into utterances and then token sentences were selected based on verb placement (verb initial, verb second and verb final). These token sentences will be analyzed for pitch placement and then compared to see if verb placement affects prosody, further expanding on the current literature which states that pitch defaults to the verb (Frazier, accepted). This research is particularly important because there is a gap in existing literature on prosody in Anishinaabemowin and there are no experimental studies such as this. References: Fairbanks, B. (2017). Ojibwe Discourse Markers. Lincoln, NE: University of Nebraska Press. Frazier, S., Déchaine, R.M, & Dufresne, M. (accepted). The Syntax of Discourse: What an Anishinaabemowin Oral Text Teaches Us. 2020 CLA Proceedings. Fujisaki, H. (1997). Prosody, models, and spontaneous speech. In Computing prosody (pp. 27-42). Springer, New York, NY. Ogimaawigwaebiik [Nancy Jones] 2013. Gakina Dibaajimowin Gwayakwaawan. In Dibaajimowinaan; Anishinaabe Stories of Culture and respect; Nigaanigiizhig [Jim Saint-Arnold] (ed.), Great Lakes Indian Fish & Wildlife Commission, 9-10.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".