The EU-Quarter as a political place: Investigating fluid assemblages in EU policy making
Bibliographic record
Abstract
We focus on the European Quarter in Brussels as a political place and the spatial context of European Union (EU) policy making. In addition to the EU institutions, the political place consists of a political agglomeration of various kinds of actors, from EU bureaucrats and politicians to a variety of stakeholders and lobbyists from all over the EU, who are permanently present in the Brussels neighbourhood. We present, firstly, the EU Quarter as a fixed setting for policy making with a relatively constant physical, locational and functional shape, and a specific sense of place as the EU bubble. Secondly, we emphasise the fragmentation and fluidity that portray it as a place divided into various political assemblages that make the place an assemblage of assemblages consisting of smaller and constantly evolving sub-processes. Thirdly, we aim to demonstrate the mobile and geographically distributed nature of EU policy making, and thus the dispersal of the political places where it takes place. This generates mobility of different kinds, which include not only the circulation of political ideas and people between different sites of the EU political system, but also the monthly migration of the Parliament and related lobbyists to Strasbourg. We believe that these three aspects of political place help the understanding of the situated but simultaneously spatially dispersed and mobile nature of EU policy making, and the study of the political places in other urban contexts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".