Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".