Practices of resistance in social media discourse. The case of Grassy Narrows First Nation
Bibliographic record
Abstract
Grassy Narrows is sadly known for one of the worst community health crises in Canada. It came to public attention in 1970 when it was revealed that an alarming number of the community members were displaying symptoms of the Minamata disease, a form of mercury poisoning, due to the huge amount of mercury dumped into the Wabigoon river from 1962 to 1970 by a chemical plant. Despite the fact that Grassy Narrows’ leaders and activists have struggled, over the years, to bring the issue to the fore, the community has not received the help they needed to face the devastating consequences of the poisoning – which are still going on nowadays. This paper takes into account the narratives emerging from one of the main micro-blogging and social networking services, namely Twitter, to attract public attention and engage wider audience. Analysis of the tweets collected by extrapolating three hashtag streams – specifically #grassynarrows, #freegrassy, #FreeGrassyNarrows – is meant to reveal forms of counter-discourse which continue the struggle for justice. The texts included in the corpus are analysed in search for the most recurrent themes through which issues relating to Grassy Narrows are framed and awareness on vital questions is created in the online and offline 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.034 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".