Seal Hunts in Canada and on Twitter: Exploring the Tensions between Indigenous Rights and Animal Rights with #Sealfie
Bibliographic record
Abstract
Background In 2014, a Twitter discussion of seal hunting, using the hashtag #sealfie, spurred a digital conflict between two rights movements—Indigenous rights in Canada and animal rights. This digital controversy touches on race, class, and geography.Analysis The hashtag’s life on Twitter obscures the two movements’ shared challenges: the undeniably neoliberal context consisting of ongoing economic struggles in northern and remote communities, and the continued loss of wildlife habitat.Conclusions and implications The authors analyze the #sealfie Twitter content generated between 2014 and 2017, exploring the tensions between the claims of the Indigenous rights and animal rights movements. They probe the failure of Twitter, and more generally social media, to generate a climate of genuine debate, and they consider how such digital platforms can serve as echo chambers for stereotypes and discriminatory discourse. Contexte En 2014, une discussion sur Twitter utilisant le mot-clic #sealfie a entraîné un conflit en ligne entre deux mouvements, l’un sur les droits autochtones au Canada et l’autre sur les droits des animaux. Cette controverse internet traita de race, classe et géographie.Analyse La présence de #sealfie sur Twitter occulta les défis partagés par les deux mouvements : le contexte indubitablement néolibéral de difficultés économiques persistantes dans les communautés nordiques et reculées et la perte continue d’habitat faunique.Conclusions et implications Les auteures analysent le contenu associé à #sealfie sur Twitter entre 2014 et 2017, explorant ainsi les tensions entre le mouvement autochtone et celui pour les animaux. Elles examinent l’échec de la part de Twitter, et des médias sociaux en général, de créer un contexte propice à de véritables débats. Elles considèrent en outre comment de telles plateformes numériques peuvent servir de caisse de résonance pour les stéréotypes et les propos discriminatoires.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".