Discursive mismatch and globalization by stealth: The fight against corruption in the Brazilian legal field
Bibliographic record
Abstract
Abstract Law and globalization studies have documented how Global South lawyers compete over the adaptation of international norms. Yet, little is known about how this adaptation legitimates worldviews beyond the law. To advance this literature, this paper proposes a discourse-centered field analysis of the legal globalization of anti-corruption ideas in Brazil. It examines Brazilian lawyers' disputes over a 2016 anti-corruption bill. The bill supporters mobilize global anti-corruption discourses that are exogenous to the legal field to defend harsher criminal law. Their critics counter the reform by mobilizing endogenous legal ideas against criminal law expansion. In so doing, they do not challenge reformers' ideas about corruption. I show how this discursive mismatch leads to a form of globalization by stealth, whereby local dynamics allow global ideas to remain unchallenged in local fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".