Unpacking the Political Effects of Social Movements With a Strong Digital Component: The Case of #IdleNoMore in Canada
Bibliographic record
Abstract
While many scholars have studied collective action with a strong social media component led by marginalized groups, few have unpacked how this form of political engagement captures the attention of established political elites and, in some cases, influences the mainstream political narrative and policy outcomes. Fewer have focused on the political impact of social media-intensive Indigenous protest movements. This article addresses these gaps in the academic literature. It does so by examining the online and offline impact of the Indigenous-led Idle No More movement at the federal level in Canada. To evaluate the movement’s effects on the public political narrative on Indigenous-related issues, this article reviews the content of the House of Commons Question Period before and after the emergence of the movement in December 2012. To measure Idle No More’s impact on policy outcomes, this article compares federal budgets and the volume of policy proposals pertaining to Indigenous Affairs introduced in the years preceding the beginning of the movement to those that came in the years following it. Semi-structured interviews with key stakeholders are also conducted to better comprehend the political impact of the movement. The study posits that protests coincided with momentary changes to the salience of Indigenous policy issues, but not with significant policy outcomes in that area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".