Building <scp>anti‐corruption</scp> agency collaboration and reputation: Hanging together or separately hanged
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.
Bibliographic record
Abstract
Abstract The implementation of preventive anti‐corruption agencies (ACAs) has been a significant public administration regulatory trend of the last two decades. This article endeavors to better understand how preventive ACAs build inter‐agency collaboration and legitimacy. Rather than analyzing ACAs in isolation, this article proposes a novel understanding of autonomy‐building by accounting for the underlying reputational dimension of their broader collaborative environment: ACAs need to strike a delicate equilibrium between defending their organizational uniqueness and effectively collaborating to tackle the complexity of corruption. To achieve this, the article employs a mixed‐methods multiple case study of four preventive ACAs in Quebec (Canada) over the last decade.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it