Poverty, corruption, trade, or terrorism? Strategic framing in the politics of UK anti-bribery compliance
Bibliographic record
Abstract
What explains longstanding UK non-compliance with international anti-bribery norms? Drawing on evidence from a comparative study of state compliance with the Organization for Economic Cooperation and Development (OECD) anti-bribery Convention and building on the literature on ‘framing’ in Sociology and International Relations, this article identifies and illustrates the impact of strategic policy framing on UK anti-bribery policy in the years following the United Kingdom’s commitment to criminalize transnational business bribes, in 1997. The research examines the way in which anti-bribery proponents and opponents framed the practice of transnational bribery differently across four distinct policy contexts in the United Kingdom: international development and poverty reduction, domestic anti-corruption, strategic trade, and—following 11 September 2001—international anti-terrorism. The analysis shows that: (a) policy advocates’ choice of frame crucially affected the timing and scope of UK anti-bribery legislation and the extent of UK (non)compliance with international anti-corruption law; and (b) the expedient frame was not necessarily the most conducive to full compliance.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".