Bibliographic record
Abstract
Images of collective action shape public understanding of social movement campaigns and issues. Modern media includes more images than ever before, and these images are remembered longer and are more likely to elicit emotional responses than are textual accounts. Yet when it comes to media coverage of collective action, existing research considers only the written accounts. This means that little is known about the extent to which images of collective action events conform to or diverge from the “protest paradigm,” a pattern of reporting found in articles that tends to marginalize protesters and legitimizes authorities. The authors address this gap by analyzing newspaper photographs of one of the most significant recent cases of Indigenous-state conflict in North America—the 1990 “Oka Crisis.” This 78-day armed standoff between Indigenous peoples and Quebecois and Canadian authorities was sparked by the attempted expansion of a golf course onto Mohawk territory. The mass media produced thousands of articles and photographs in their coverage of the event. This article uses these photographs to assess the manner in which images frame collective action and collective actors. The authors find that images of collective action frame these events differently and in a more nuanced way than do textual accounts. For example, while challengers are just as likely to be shown in images of collective action, they are less likely to be specifically named. In addition, officials are more likely to be shown in dominant positions, but certain groups of officials (particularly government representatives) are also the most likely to be shown as emotional and angry. These findings illustrate the sometimes conflicting messages depicted in images of collective action.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.005 |
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".