Bibliographic record
Abstract
Most of the work done in grammaticography focuses on the writing of grammars for an audience of linguists, and more specifically, typologists. In this paper, we present a grammaticographic model designed mainly to take into account the needs of minority language speakers, because they play a central role in the preservation of their language. However, since in minority language situations it is not possible to generate as many grammars as there are different potential end users, we propose a multilevel grammar, based on our experience as grammarian of Innu, a First Nation language spoken in Quebec (Canada). In this type of grammatical description, the first (main) level is addressed to non-specialist users, the speakers of the language being described, whereas grammatical material aimed at other users (such as linguists) is presented in secondary levels and is limited to core information. Our grammaticographic model was initially conceived for paper (printed) grammars, but we believe that electronic publication offers interesting solutions for multilevel grammars, while paper (printed) grammatical descriptions have greater limitations.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".