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 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 | 0.003 |
| 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) | 0.002 | 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".