Agonistic failures: Following policy conflicts in Berlin’s urban cultural politics
Bibliographic record
Abstract
The paper intervenes in critical policy studies to challenge the ‘success bias’ lingering in public policy accounts of collaborative governance. I suggest conflict, rather than consensus, is a productive resource to navigate collaborations between state and civic stakeholders. By developing a conflict-oriented framework that foregrounds political decisions as always-already failing – regardless of whether promoted as success or failure – I argue that the recognition of nuanced conflicts contributes to new understandings on what counts as success or failure to whom. To substantiate the conflict-oriented framework of policy failure, I present empirical insights into Berlin’s urban cultural politics, shedding light on a new funding instrument for artists. Unpacking artists’ and administrators’ understandings about what constitutes a failure, and how to proceed from there, I propose ‘policyfailing’ as ongoing failure. Conceptualising failure along the lines of operational conflicts (i.e. concrete, procedural disagreements) and meta conflicts (i.e. overarching, ideological differences), two scenarios of policy failure emerge: absolute policy failure, pointing to unsolvable conflicts between state and civic stakeholders; and agonistic policy failure, referring to wider-ranging disagreements about the purpose of policy issues, which are however transferred into temporary policy solutions. Following one such agonistic policy failure in Berlin over time, I show how new opportunities for both absolute and agonistic policy failure unfold. Ultimately, I outline the practical, political and analytical potential of an agonistic framework to understand policies as inherently contested and, to some degree, always failing.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.051 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| 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".