Multisectoral Movement Alliances and Media Access: Salvadoran Newspaper Coverage of the Health Care Struggle
Bibliographic record
Abstract
Abstract Focusing on the social movement that resisted the privatization of health care in El Salvador in 2002–3, this article asks how the movement's multisectoral composition influenced news coverage of the health care policy debate. Specifically, it examines whether the diversity of perspectives in the alliance was reflected in the media's source selection and framing of the policy issues. A content analysis of Salvadoran newspapers' coverage shows that the media relied mainly on just two movement actors to represent the antiprivatization position: the striking doctors and the leftist opposition party. It also reveals that a period of elite dissensus on the policy issues opened a temporary opportunity to insert movement messages in the coverage. The study indicates that a multisectoral alliance does not enhance movement influence through the news media, though broad alliances confer strategic advantages for the movement's broader communication work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".