Understanding and Taming Public and Private Corruption in the 21st Century (November 2014)
Bibliographic record
Abstract
The conference looks at corruption in business, finance, government and enforcement.During the past few years every one of those sectors in Canada and internationally has demonstrated a capacity for corruption.Canada is not alone in falling victim to diverse corruption schemes and not alone in looking for answers.Our conference is an academic exercise to explore the vulnerabilities to corruption, the systems that undermine anti-corruption intentions and the failures of transparency and accountability regimes.We are pleased to have attracted some of the international experts to our conference who can reflect on their experiences and their attempts at solutions.Key themes that will be explored include:Corruption in undermining international economic development Corruption as a facilitator of organized crime Corrupt interactions between business and government Corruption within Government Institutions The U.N. Convention against Corruption (UNCAC) The Foreign Corrupt Practices Acts and their extra territorial reach Questioning the adequacy of whistle-blowing legislation, policies and practices
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".