Understanding and Taming Public and Private Corruption in the Twenty-First Century
Bibliographic record
Abstract
We are pleased to present these articles that were originally presented at a symposium held at Osgoode Hall Law School on 6–7 November 2014.1 Our objective was to offer a symposium that looked at corruption from diverse perspectives, with a broad national and international focus on business, financial, governmental, private sector, and enforcement corruption. Both the Symposium and the compilation of this special issue of the Journal were unique. They required an interplay between contributions from professionals working on the ground in various countries around the world (such as practitioners working in the World Bank, the Inter-American Development Bank, and Transparency International Canada, as well as police policy analysts and investigators working with various non-governmental organizations, partners in law firms, and investigative journalists) and academics who submitted scholarly articles based on their research pertaining to the phenomenon of corruption. Four academic articles form the body of this special issue. In addition, several professionals share their experiences and knowledge of the lived impact of corruption around the world; their contributions are found in the section entitled Supplement: Practitioners’ Perspectives. In this manner, we were able to incorporate not only scholarly peer-reviewed contributions but also, and of equal significance, the more practical knowledge and experiences of professionals working in the field.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".