Hijacking legality: Corruption and property creation in Brazil's frontiers
Bibliographic record
Abstract
Corruption undermines natural resource governance and conservation efforts, fueling deforestation and violence against land and environmental defenders. To address this complex issue and avoid stereotypical and colonial perspectives, the literature in political ecology and geography has called for shifting the focus from legal definitions of corruption to an emphasis on power dynamics. While this approach has led to innovative analyses, it has often pushed the question of legality into the background. In this paper, we bring law back to the forefront and analyze the relationship between corruption and landed property. We examine land tenure laws and regulations, along with 15 inquiries into corruption in land-grabbing schemes in Brazil's two main agricultural frontiers, the Amazon and the Matopiba. We argue that, in resource frontiers, corruption and legality co-constitute each other. Corruption hijacks different legal processes establishing and protecting landed property. Regarding the recognition of land claims and property formation, land grabbers can (1) dismantle policies recognizing competing land uses to make them illegible; and (2) capture tenure formalization policies to legalize land grabs. Likewise, processes intended to protect property rights and ensure legal certainty are vulnerable to hijacking, as (3) land registries and cadastres can be defrauded to distort property rights; and (4) law enforcement mechanisms can be co-opted to enact abusive land claims. Therefore, we reject the notion that legality precedes corruption in resource frontiers. Instead, these findings suggest a recursive relationship between corruption and legality. The uncritical use of legal dichotomies to guide understandings of corruption can reproduce legalized abuses.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".