May We Have Your Attention Please? Human-Rights NGOs and the Problem of Global Communication
Bibliographic record
Abstract
Historically, nongovernmental organizations (NGOs) have relied on mainstream news media to expose human-rights violations and encourage governments to pressure the perpetrators. Thanks to the Internet, NGOs are crafting new strategies for conducting information politics. Despite the obvious democratization of access to the means of communication, however, the new media may in fact represent a more challenging environment in which to be heard for some groups seeking global attention. We draw on agenda-setting research to develop a theory of global attention bottlenecks and use it to explain the success of 257 transnational human-rights groups at generating attention in both international mainstream news media and social media outlets. We conclude that most NGOs lack the organizational resources to compete effectively for either traditional news coverage or for public attention and that the Internet is unlikely to resolve the problem of global communication.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".