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Record W2528687542 · doi:10.60082/2817-5069.2979

Corruption and Development: The Need for International Investigations with a Multijurisdictional Approach Involving Multilateral Development Banks and National Authorities

2015· article· en· W2528687542 on OpenAlexvenueaboutno aff
Juan Ronderos, Michelle Ratpan, Andrea Osorio Rincon

Bibliographic record

VenueOsgoode Hall law journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsnot available
FundersInter-American Development BankWorld Bank GroupU.S. Department of Justice
KeywordsSanctionsEnforcementForeign Corrupt Practices ActLanguage changeMultinational corporationPunitive damagesBusinessLegislationNational developmentForeign nationalInternational tradePublic administrationPolitical scienceEconomicsLawEconomic growthFinance

Abstract

fetched live from OpenAlex

We argue that while Multilateral Development Banks (“MDBs”) and national governments have mechanisms to fight corruption, the objectives and outcomes of these enforcement mechanisms diverge. MDBs are interested in the causes and effects of corruption from a development perspective and, as such, tend to sanction small and medium enterprises and individuals, while national governments are focused on a more punitive outcome, targeting larger multinational corporations. This article examines the enforcement objectives articulated in national legislation, namely the US Foreign and Corrupt Practices Act and its Canadian counterpart, the Corruption of Foreign Public Officials Act, as well as several Canadian cases, on the one hand, and the tools and outcomes of MDBs’ sanctions systems on the other. We conclude that national enforcement efforts and MDBs’ sanctions outcomes intersect in their fight against international corruption in that their results are complementary; the former punishing large-scale offenders while the latter ensuring the integrity of development projects.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
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 head0.093
GPT teacher head0.299
Teacher spread0.206 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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