#KeepOurLanguagesStrong: Indigenous Language Revitalization on Social Media during the Early COVID-19 Pandemic
Bibliographic record
Abstract
Indigenous communities, organizations, and individuals work tirelessly to #KeepOurLanguagesStrong. The COVID-19 pandemic was potentially detrimental to Indigenous language revitalization (ILR) as this mostly in-person work shifted online. This article shares findings from an analysis of public social media posts, dated March through July 2020 and primarily from Canada and the US, about ILR and the COVID-19 pandemic. The research team, affiliated with the NEȾOLṈEW̱ “one mind, one people” Indigenous language research partnership at the University of Victoria, identified six key themes of social media posts concerning ILR and the pandemic, including: 1. language promotion, 2. using Indigenous languages to talk about COVID-19, 3. trainings to support ILR, 4. language education, 5. creating and sharing language resources, and 6. information about ILR and COVID-19. Enacting the principle of reciprocity in Indigenous research, part of the research process was to create a short video to share research findings back to social media. This article presents a selection of slides from the video accompanied by an in-depth analysis of the themes. Written about the pandemic, during the pandemic, this article seeks to offer some insights and understandings of a time during which much is uncertain. Therefore, this article does not have a formal conclusion; rather, it closes with ideas about long-term implications and future research directions that can benefit ILR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".