Translations for Our Nations: Addressing the Indigenous Language Gap in COVID-19 Health Communication
Bibliographic record
Abstract
Purpose: The availability of culturally safe and plain-language resources is necessary to reduce the spread of COVID-19 for Indigenous communities around the world. Translations For Our Nations is an initiative addressing these resource gaps, making available COVID-19 health resources in Indigenous languages on the web. The project began in April 2020 as a result of the Indigenous COVID-19 Health Partnership launched by Victor A. Lopez-Carmen, a Dakota and Yaqui medical student, Harvard Medical School) and co-founded by Sterling Stutz and Thilaxcy Yohathasan, (MPH-Indigenous Health at the University of Toronto), and Sukhmeet Singh Sachal (medical student, University of British Columbia). Methods: Translators from Indigenous communities around the world signed up to participate in the project via a GoogleForm in April 2020. Over 100 Indigenous translators and community members in regions (South America, Asia, Africa, Europe, North America, and the Pacific) were provided the 5 English language source materials reviewed by physicians and Indigenous youth leaders. Translators submitted their translated documents via email and on September 1, 2020 the website Translations4OurNations.org was launched where the translated documents can be accessed and downloaded with more translations accepted on a rolling basis. Results: Translations for our Nations has published COVID-19 health resources in 40+ Indigenous languages from around the world. The website also includes photos and text submissions from community members speaking to the importance of culturally-specific COVID-19 health information disseminated directly to communities in local languages and dialects. Implications: Indigenous Nations have the right to access vital health information in their mother tongue. This project is led by and designed for Indigenous youth and Indigenous community members to empower individuals and communities to make informed choices regarding their health and exposure risks, and decrease the risk of COVID-19 transmission in Indigenous communities around the world.
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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 | 0.065 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".