Media Portrayals of Hashtag Activism: A Framing Analysis of Canada’s #Idlenomore Movement
Bibliographic record
Abstract
The confluence of activism and social media—legitimized by efforts such as the Arab Spring and Occupy Movements—represents a growing area of mainstream media focus. Using Canada’s #IdleNoMore movement as a case, this study uses framing theory to better understand how traditional media are representing activism borne of social media such as Twitter, and how such activism can ultimately have an impact in political and public policy debates. A qualitative framing analysis is used to identify frames present in media reporting of #IdleNoMore during its first two months by two prominent Canadian publications. Emergent frames show that hashtag activism as a catalyst for a social movement was embraced as a theme by one of the publications, therefore helping to legitimize the role of social media tools such as Twitter. In other frames, both positive and negative depictions of the social movement helped to identify for mainstream audiences both historical grievances and future challenges and opportunities for Canada’s First Nations communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".