A processual framework for understanding the rise of the populist right: the case of Brazil (2013–2018)
Bibliographic record
Abstract
Abstract How and in what sequence do social structures, contingent events, and agents’ decisions combine over time to bring about a new populist right? To answer this question, we propose a framework to analyze social processes spanning three levels of analysis: global political economy, national political articulation, and subnational political geography. We challenge static theories that focus solely on the “supply and demand” for populism, as well as purely contingent accounts of a “perfect storm.” Instead, we argue that processes across these three levels link together in causal chains to produce an “ecosystem” of right-wing populist support. To specify this framework, we analyze the ascendance of Jair Bolsonaro to the presidency of Brazil between 2013 and 2018, drawing upon quantitative macroeconomic and protest event data, qualitative interview and archival data collected from private sector actors and social movements, and geo-spatial electoral data. Finally, we probe the generalizability of this analytical framework through a discussion of secondary work on recent cases of right-wing populism in the Global South. By focusing on the dynamic connection of inter-scalar processes over time, we illustrate how our framework paves the way for further conjunctural analyses of the current right-wing populist upsurge.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".