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Record W3154957815 · doi:10.24908/iqurcp.14552

Prosodic Interactions in Anishinaabemowin Verbs

2021· article· en· W3154957815 on OpenAlexvenueaboutno aff
Sonja Frazier

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCultural, Linguistic, Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsVerbProsodyMeaning (existential)SyntaxIntonation (linguistics)PsychologyUtteranceModal verbComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.227
GPT teacher head0.454
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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