Cohesion, consensus, and conflict: Technocratic elites and financial crisis in Mexico and Argentina
Bibliographic record
Abstract
Observers of economic policy-making in developing countries often suggest that consensus and cohesion within technocratic policy elites facilitate the implementation and consolidation of reforms, but have not clearly defined these terms or the relationship between them. Likewise, political sociologists argue that social networks account for elite cohesion, but have not adequately specified the relevant structural properties of these networks. This article argues that structural network cohesion facilitates elite consensus formation by enabling cooperation, while fragmented networks promote competition between factions and hence conflict. I support this hypothesis empirically by examining two cases in which elite consensus was severely challenged by financial crises: Mexico and Argentina. Mexican policy elites sustained consensus throughout the crisis, whereas conflict plagued the Argentine elite. Likewise, while the Mexican technocratic elite is highly cohesive, the Argentine elite is fragmented and factionalized. I document this hypothesis using a mixed-methods approach that embeds an analysis of elite networks within a comparative analysis of policy-making patterns drawing on extensive fieldwork in both countries.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".