Enacting Citizenship in an Urban Borderland: the Case of Maximilian Park in Brussels
Bibliographic record
Abstract
This paper explores the relationship between asylum seekers and Maximilian Park, a contested site in Brussels in terms of unresolved conflicts around migration, refugees and borders. By tracing the park’s evolution as part of the North Quarter, and understanding the various transient trajectories that characterize this urban area, the paper will probe into the interaction between “full” and “temporary” citizens. Through spatial synthesis and mapping, the paper will first unpack the urban history of the North Quarter as part of the arrival infrastructure of the European capital. The connections between groups with varying degrees of vulnerability who claim spaces with more or less legitimacy will be explored through two main sources complementing ethnographic analysis. Firstly, narratives developed by the local press will be used as a means to unfold the main perspectives when dealing with the complex topic of migration and public space; secondly, the on-line organization of a key civil society organization active in the support of migrants will be interrogated. Building on the notion of “non-citizen citizenship” the authors will conclude by critically reflecting on what form the extension of rights could take to help craft a revised form of citizenship based on the politics of presence in the city.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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".