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Record W3034765773 · doi:10.24908/iqurcp.14025

Prosody of Ojibwe Discourse Markers

2020· article· en· W3034765773 on OpenAlexvenueno aff
Sonja Frazier

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSentenceLinguisticsProsodyDiscourse markerHeadlineActive listeningPragmaticsConversationNarrativeComputer sciencePsychologyCommunicationPhilosophy

Abstract

fetched live from OpenAlex

Discourse markers (DMs) are optional, sequentially dependent sentence-initial items (Schiffrin, 1987) that are used to bracket units of talk (e.g. oh, well, because, y’know, now ). This research aims to better understand Ojibwe DMs which typically occur as the first or second element of a sentence (Fairbanks, 2016). The proposed analysis seeks to understand the prosody of Ojibwe DMs broadly and specifically their use in narrative structure. The data is drawn from Gakina Dibaajimowin Gwayakwaawan ( All Teachings are Correct ) by Nancy Jones, 2013. The analysis was done by using the programs Audacity and PRAAT to identify individual sentences and their pitch prominences. Through careful listening and pitch tracking, prosodic properties of DMs were found to indicate the following: DMs attract the most prominent pitch in the sentence. DMs are used by the speaker to attract the hearer’s attention; in this sense they are interactional (Franks-Job, 2006). DMs are used by the speaker to structure the narration; as such they interact with topic changes and emphasis (Lenk, 1998) This study creates a more complex picture of Ojibwe DMs and adds to our understanding of the language. References: Fairbanks, B. 2016. Ojibwe Discourse Markers. University of Nebraska Press. Franks-Job, B. 2006. A dynamic-interactional approach to discourse markers. In Approaches to discourse particles, K. Fischer (ed.) pp. 395–413. Amsterdam: Elsevier. Lenk, U. 1998. Discourse markers and global coherence in conversation. Journal of Pragmatics 30(2):246-257 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. Raso, Tommaso. 1996. Prosodic constraints for discourse markers. Spoken Corpora and Linguistic Studies. In Spoken Corpora and Linguistics Studies , T. Raso & H. Mello (eds.) 411-467. Benjamins: Amsterdam. Schiffrin, D. 1987. Discourse Markers. doi: 10.1017/cbo9780511611841.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0020.000

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 head0.151
GPT teacher head0.381
Teacher spread0.230 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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