What Made People Take to the Streets? A Study on the Determinants of Protests in Brazil Based on Google Trends Data
Bibliographic record
Abstract
In the literature on political participation through contestation, the apparent difficulties that can hinder the realization of protests are extensively debated. The high costs associated with the act of protesting can make it an option of political participation mainly associated with the high class. The importance of certain institutional frameworks that more easily give vent to a society that protests is also discussed. Psychological variables are as well explored in this area of Political Science: lacking the horizon of possibilities, certain societies can become distrustful of the efficiency of protest, submitting to the status quo, thus creating a dulled scenario of normality in the face of socioeconomic injustices. This work intended to study the determinants that could overcome these adversities in the Brazilian context. It was concluded in favor of the thesis of the mobilizing event as a relevant channel to incite protests – such as the increase in bus fares in 2013, and the World Cup in 2014. In 2015, however, there is a change in the mobilizing agenda, leading us to the second factor that most easily mobilizes Brazilians: the blaming of personalities or institutions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".