Protest events in international press coverage: An empirical critique of cross-national conflict databases
Bibliographic record
Abstract
The empirical analysis of protest events and other expressions of social conflict is one of the core tasks of the discipline of comparative sociology. The numerous international data sets and empirical country comparisons that rely exclusively on reporting in English-language newspapers such as The New York Times in surveying protest events nevertheless suffer from considerable distortions. Using the example of some 1800 protest events in Argentina, Mexico and Paraguay in the year 2006, the present study shows that there are remarkable differences between national and international (English-language) newspapers when it comes to frequency of reporting. On the one hand, a mere 5.3 percent of all protest events that are reported nationally also attract the attention of the international press. On the other hand, the percentage of international reporting depends considerably and to a statistically significant extent on the country in which the protest takes place. Besides, these country differences persist when additional protest characteristics (e.g. the number of participants, the participation of renowned personalities and the escalation of the protest into rioting) are controlled by means of multivariate logistic regressions. The measurement error that results when surveying protest events on the strength of coverage in the English-language international press is therefore not constant across countries. The frequently used data sets like the World Handbook of Political and Social Indicators or the Cross-National Time Series Data Archive launched by Arthur Banks are thus proving to be highly questionable sources for international comparisons in protest and conflict research.
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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 | 0.237 | 0.668 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.042 | 0.112 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".