Canada is #IdleNoMore: exploring dynamics of Indigenous political and civic protest in the Twitterverse
Bibliographic record
Abstract
Social media have been playing a growingly important role in grassroots protest over the last five years. While many scholars have explored dynamics of political cyberprotest (e.g., the ongoing transnational Occupy movement, the 2012 Quebec student strike, the student-led protest movement in Chile between 2011 and 2013), few have studied sub-dynamics relating to ethno-cultural minorities’ uses of social media to gain visibility, mobilize support, and engage in political and civil action. We fill part of this gap in the academic literature by investigating uses of Twitter for political engagement in the context of the Canada-based Idle No More movement (INM). This ongoing protest initiative, which emerged in December 2012, seeks to mobilize Indigenous Peoples in Canada and internationally as well as their non-Indigenous allies. It does so by bringing attention to their culture, struggles, and identities as well as advocating for changes in policy areas relating to the environment, governance, and socio-economic matters. Our study explores to what extent references to aspects of Indigenous identities and culture shaped INM-related tweeting and, by extension, activism during the summer of 2013. We conducted a quantitative and qualitative content analysis of 1650 #IdleNoMore tweets shared by supporters of this movement between 3 July 2013 and 2 August 2013. Our study demonstrates that unlike other social media-intensive movements where economic and political concerns were the primary drivers of political and civil engagement, aspects of Indigenous culture influenced information flows and mobilization among #IdleNoMore tweeters.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".