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Record W2908505392 · doi:10.6092/issn.2612-0496/8517

Enacting Citizenship in an Urban Borderland: the Case of Maximilian Park in Brussels

2018· article· en· W2908505392 on OpenAlexaboutno aff
Racha Daher, Viviana d’Auria

Bibliographic record

VenueLirias (KU Leuven) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
FundersEuropean Commission
KeywordsCitizenshipLegitimacyPolitical scienceRefugeeQuarter (Canadian coin)NarrativePoliticsNexus (standard)Capital (architecture)EthnographyCivil societySociologyGeographyLawEngineeringAnthropology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.344
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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