Event-focused network analysis: a case study of anti-corruption networks
Bibliographic record
Abstract
Abstract Research on diffusion and transfer increasingly relies on the concept of policy networks, but often in inductive, descriptive, and anecdotal ways. This article proposes a more robust method for the comparative analysis of policy networks, a method we term ‘event-focused network analysis’ (EFNA). The method assumes that networks are most clearly revealed in ‘events’ – conferences, meetings, workshops, etc. Databases of participants at these events provide the foundation for social network analysis of the networks of which they are part. The Organisation for Economic Co-operation and Development (OECD) has hundreds of such events annually that are connected to a myriad of policy issues, thus allowing cross-sectoral network comparisons. The article begins with a review and critique of current approaches to network analysis, explains the EFNA approach, and then applies it to anti-corruption networks centred in the OECD. The case study shows the promise of the method, particularly in being able to trace a wider range of actors than is typical, taking us beyond the ‘usual suspects’ in conventional transfer studies.
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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 | 0.008 | 0.014 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".