Bibliographic record
Abstract
This paper outlines a model of community collaborative research in which the linguist aligns documentary research with the needs and linguistic capacities of a community that is trying to reverse language shift. The linguist working on a grammar of the language responds to community topics and questions as they arise. At the same time, the writing of the grammar crucially involves members of the community as assistants. We discuss one project of this type, where the goal is the production of a grammar designed for use both by linguists and members of the community. This follows in the growing tradition of writing high quality reference grammars which can be used by speakers of that language (c.f. Valentine 2001). A community reference grammar for Inuktitut has as its primary goal that the grammar be broad ranging in topics, extending beyond the pages of inflectional paradigms which characterize most grammars of this language. Issues such as WH questions and how quantifiers are used will be included. A second goal is that the grammar be of immediate and direct use to the community. In order to ascertain that the grammar is readable and therefore useable by members of the community, the linguist must obtain feedback from language professionals (translators, language teachers, curriculum developers) within the community. This is done through the internet, where draft sections of the grammar are posted. Assistants have been hired to serve as readers and liaison to the other interested members of the community. This has two secondary purposes. Errors or omissions in the discussion are caught; the online drafts serve as a catalyst for discussion about language within the community and on the radio. A linguist is unlikely to be aware of what language professionals within the community actually need and want to know at any particular moment. In one recent case the linguist was asked to identify a set of elements. These elements are possessive suffixes which have a complex set of portmanteau features, including a) person and number of possessor, b) number of possessum and c) case. There is also some slight variation within the community. The question led to a draft entry on this topic, which is being read by the liaison assistants. In this manner, the construction of a reference grammar is one intersecting piece in the overall community effort to reverse language shift.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.318 | 0.210 |
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".