Event-focused network analysis: a case study of anti-corruption networks
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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