MétaCan
Menu
Back to cohort
Record W4281252944 · doi:10.1093/ajcl/avac008

Using Criminal Law to Fight Corruption: The Potential, Risks, and Limitations of Operation Car Wash (<i>Lava Jato</i>)

2021· article· en· W4281252944 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe American Journal of Comparative Law · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLegal and Constitutional Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImpunityLanguage changeJurisprudenceCognitive reframingLawPoliticsPolitical scienceScope (computer science)LavaCriminal lawOrganised crimeState (computer science)Relation (database)Law and economicsSociology

Abstract

fetched live from OpenAlex

Abstract The Brazilian case of Lava Jato started with a scandal involving the massive malfeasance of corporate and political elites in relation to the state-run oil company Petrobras. The scope of the corruption was unprecedented. Politicians and Petrobras employees received hundreds of millions (if not billions) of dollars in kickbacks between 2004 and 2012. This Article focuses on the innovations promoted by the Lava Jato case. This new jurisprudence has not only played a key role in breaking a long-lasting tradition of impunity in Brazil, but it has also generated much controversy. On the one hand, many Brazilian citizens welcomed the changes, as they allowed judges to overcome the obstacles faced by courts in previous corruption cases. On the other hand, opponents argue that the case is not solidly grounded in rule of law principles. Instead of taking sides in this debate, this Article tries to reframe it by arguing that there may be benefits associated with these novel interpretations, but there may also be costs and risks.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.211
GPT teacher head0.320
Teacher spread0.110 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it