Bibliographic record
Abstract
General frameworks for glossing linguistic examples (Lehmann 1982, 2004 and particularly the Leipzig Glossing Rules (LGR) by Comrie, Haspelmath, and Bickel 2008, 2015) aim to make the sharing of grammatical information more efficient, consistent and intelligible. While they have improved grammatical communication for many languages, language-family specific facts and conventions can be difficult to integrate into cross-linguistic frameworks. In response to this difficulty for Baltic languages, Nau and Arkadiev (2015) have suggested a general framework for the glossing of the languages of that family. In the spirit of that work, the purpose of this article is to bring up some issues in interlinear glossed text (IGT) in Dene languages and give the rationale for possible solutions. We acknowledge that establishing a glossing standard for Dene, with close to 40 languages in the family, is a much more difficult, maybe even impossible task compared to doing so for the two languages of the Baltic family. But as a step towards doing so, we would like to continue the conversation about glossing Dene languages initiated by Holden (2013) and Kibrik (2019), in order to promote better analytical communication within our subfield and to linguists in general.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".