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Record W2971445457 · doi:10.1080/13527258.2019.1663237

Politics of belonging in Brussels’ European Quarter

2019· article· en· W2971445457 on OpenAlexaboutno aff
Tuuli Lähdesmäki

Bibliographic record

VenueInternational Journal of Heritage Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsnot available
FundersKulttuurin ja Yhteiskunnan Tutkimuksen ToimikuntaHorizon 2020 Framework ProgrammeAcademy of FinlandEuropean Commission
KeywordsQuarter (Canadian coin)PoliticsNarrativeEuropean unionCollective memorySociologyIdentity (music)AestheticsHistoryPolitical scienceMedia studiesLawLiteratureArchaeologyArt

Abstract

fetched live from OpenAlex

The European Union (EU) has been criticised for a lack of imageries and sites of memory that nation-states have traditionally utilised in their identity-building. The EU, along with other actors, has responded to this iconographic deficit with memory and heritage initiatives and branding campaigns. This article explores how this deficit is dealt with in the European Quarter in Brussels by enlivening it through cultural regeneration and creating narratives that link Europe’s and the EU’s past with the present. The article utilises hermeneutic phenomenological approach combining observation and interpretation of diverse place-making practices, such as monuments, memorials, public artworks, history plaques, and naming of administration buildings, with close reading of the EU’s marketing and promotional material. It examines how a feeling of belonging to Europe and the EU is advanced in the Quarter and how it is sought to be turned into a European collective place. The article indicates how the Quarter’s politics of belonging ignores various layers of meanings related to its history and present. The Quarter’s invitation to belong is selective as its narratives focus on male actors and ignore colonial references and today’s multi-ethnic reality in the neighbourhood.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.337
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.037
GPT teacher head0.284
Teacher spread0.246 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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