An Open-Access Toolkit for Collaborative, Community-Informed Dictionaries
Bibliographic record
Abstract
ABSTRACT: In this article, we discuss the development of a relational lexicography framework and an open-access toolkit for collaborative, community-informed dictionaries. We explain how the relational lexicography toolkit supports envisioning, developing, and publishing dictionaries that meet the cultural, linguistic, and educational goals of Indigenous communities who, despite ongoing language shift, are working to strengthen their languages. This framework recognizes the many relationships that are present in community-based language projects, including relationships between speakers, dialects, academics, communities, and the dictionary itself. The toolkit, which comprises two online Knowledgebase of Indigenous language dictionaries and lexicography technologies, documents how these relationships have been represented in existing work and provides a centralized platform to refer to and compare resources. Overall, this article provides background context on our methodology, project development, aspects of the resulting resources, and the anticipated benefit of creating such an open-access framework in support of community-based lexicography.
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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 | 0.028 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".