Ḵaḵotłatłano'xw xa ḵwaḵwax 'mas: Documenting and reclaiming plant names and words in Kwakwala on Canada's west coast
Bibliographic record
Abstract
This paper describes the process and outcomes of a project focused on community centred reclamation of plant-based knowledge in the Kwak̓wala language from previously published materials as well as new documentation with Kwak̓wala speaking Elders. The paper describes our research process resulting in the documentation of 300 plant word names and phrases, starting with 135 plants with names and words in Kwak̓wala that had been documented between the late 19th and early 20th century by Franz Boas and George Hunt, subsequently added to and enriched by community members and academics. An audio-visual dictionary of these plant names and associated phrases is now available through the FirstVoices web portal (http://bit.ly/LDC_FirstVoices). The corresponding author initiated the work and then further developed the research in collaboration with Kwakwa̱ka̱'wakw fluent speakers, linguists, biologists, and the U'mista Cultural Society. The project has stimulated interest among community members who provided valuable feedback on the different ways in which this research can be further accessed and then delivered. The paper concludes with some structured reflections on how to proceed in community-led research projects such as this. The authors see further opportunity for continued cross-disciplinary and community-based research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".