Conflicts of Interest and the Importance of the Organizational Variable: A Comparison Among Canada, the United States and Mexico
Bibliographic record
Abstract
The aim of this document is to advance our understanding of the costs and perils faced by any country when looking for tackling and possible conflict of interests among public officials. In effect, there are regulatory, organiza- tional, and institutional difficulties and costs related with the implementation of reforms aimed to combat or prevent and potential conflicts of interest. This discussion is vitally important above all to developing countries such as Mexico given that the effectiveness, cost and impact of this tool up until now applied to different countries has achieved rather heterogeneous results. The main objective of this paper is to enhance the importance of the organizational dimension whenever a regulatory framework to control conflict of interests is placed or implemented. Public organizations are not merely instruments adaptable to the orders and instructions stemming from regulations and rules. In this sense, the regula- tory (both formal and soft) framework should take in consideration the concrete organizational effects of the rules and institutions designed to change the behavior of actors. Developing a comparison of regulatory, institutional and organiza- tional strategies applied in Canada, Mexico and USA we seek to show that the organizational dimension is critical in order to understand the real net effect achieved when dealing with complex behaviors like the ones which drive social and po- litical actors to face conflict of interests situations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 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".