MECHANISMS OF CONFLICT MANAGEMENT IN EU REGULATORY POLICY
Bibliographic record
Abstract
In this conceptual article, we explore mechanisms of conflict management in European Union (EU) regulatory policy-making. We build on J.G. March's distinction between aggregation and transformation as the two strategic options to deal with inconsistent preferences or identities that are at the source of social conflict. While this distinction is helpful in mapping conflict management mechanisms, the rigid association of these two options with the rival paradigms of rationalism and constructivism respectively has led political scientists to neglect conflict management strategies that work at the edges of aggregation and transformation. We show the potential of these latter strategies as intelligent ‘in-action’ hybrids that emerge from ground-level policy-making praxis of actors navigating a complex institutional and policy environment. Specifically, we discuss five strategies: issue-based aggregation; arena-based aggregation (arena-shifting and arena-creation); socialization; re-framing; and proceduralization, their underlying mechanisms and related scope conditions. The theoretical implications of this discussion lead us towards ‘strategic constructivism’. In the conflict management mechanisms that are of most interest, norms and ideational structures matter, but they are related to strategic actors who draw on and orchestrate ‘ideas’ in pursuit of political goals.
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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 | 0.034 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".