Interactional governing activities: A novel perspective on how actors co‐develop field governance
Bibliographic record
Abstract
Abstract We advance a novel perspective to study how field actors co‐develop field governance through continuous interactions. Field governance determines the formal and informal rules of a field, defining membership boundaries and core practices. Prior research has mostly studied the establishment of top‐down regulations or the work of advocacy and social movement organisations to influence or overthrow existing regimes. We review 147 previously disconnected articles on field governance and institutional work and identify interactional governing activities (IGAs), the concept we advance and define as the strategic and interactional activities actors deploy to develop, disrupt and maintain field governance. Depending on field conditions, we propose that actors combine IGAs in various interaction modes to either oppose the existing order, lobby for change or collaborate to jointly develop field governance. We contribute to the scholarly understanding of field governance development by proposing a continuous process that extends beyond influencing regulatory decision‐making to include knowledge‐building and interactional infrastructure‐development activities. Our study provides novel insights on collaborative institutional work for field governance co‐development by heterogeneous actors. By defining and categorising IGAs, we contribute to both a more integrative theoretical understanding of field governance as well as a playbook for practitioners, collective interest organisations and regulators engaged in field‐building work.
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.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".