Indigenous-language communication as an opportunity for engagement in the aquatic sciences
Bibliographic record
Abstract
Knowledge gained from the aquatic sciences is of relevance to Indigenous communities. Efforts are underway to braid Indigenous and western ways of knowing, following an overdue increased focus on reconciliation and calls to action. While many collaborative projects involve informal Indigenous-language communicative outputs, the case has been made for Indigenous-language communication outputs from scientific projects that are not essentially collaborative. Here, we describe our recent efforts to communicate relevant aquatic science topics from originally non-collaborative projects in Mi'kmaw, an Indigenous language of eastern North America. We created an infographic that details the mercury cycle in Mi'kmaw by coining or reworking terms, avoiding technical language that is known to hinder science communication. This kind of knowledge mobilization shows that it is possible to communicate scientific findings in an Indigenous language to engage with an Indigenous audience. The benefits gained from doing this include addressing calls to action, language revitalization, and better inclusion, motivation, engagement, and understanding among Indigenous language speakers. In demonstrating the benefits of this type of science communication in projects not originally designed with two knowledge systems in mind, through the example of the mercury cycle, we hope that other such projects may incorporate Indigenous language into their collaborations.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".