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Record W2043756868 · doi:10.3727/109830409787556639

A Contested Ethnic Tourism Asset: The Case of Matonge in Brussels

2009· article· en· W2043756868 on OpenAlexaboutno aff
Anya Diekmann, Géraldine Maulet

Bibliographic record

VenueTourism Culture & Communication · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsTourismQuarter (Canadian coin)Promotion (chess)Ethnic groupScrutinyFlourishingTourism geographyEconomic growthCultural tourismMulticulturalismMarketingAsset (computer security)BusinessPolitical scienceGeographyEconomics

Abstract

fetched live from OpenAlex

Brussels, often referred to as the Capital of Europe, has a flourishing "African" quarter called Matonge (named after a quarter of Kinshasa, Congo) that is becoming an important tourist destination within Brussels. On the up since the 1960s, Matonge can boast African food stores, clothes boutiques, and hairstylists as well as African bars and restaurants. Yet the district is a multicultural one, with the sub-Saharan community being only one user group among many. For about a decade the area has attracted tourists and features in private guided tour programs and guide books as an ethnic tourism destination with an African flavor. Tourism authorities have ignored this development for a long time. However, due to the various changes in urban tourism demand, they have recently included Matonge in their tourism promotion initiatives by creating an itinerary in the quarter. This article looks at certain underlying issues that may either boost or hinder tourism development. Through an integrated approach based on in-depth interviews with stakeholders and surveys with shop owners and passers-by, it tackles decision-making processes and public policies related to the tourism development of the area. Furthermore, the article identifies the different user groups and analyzes the role of the community under scrutiny and their perception of the development.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score1.000

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)0.0010.000

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 head0.058
GPT teacher head0.295
Teacher spread0.237 · 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 designNot applicable
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

Citations5
Published2009
Admission routes1
Has abstractyes

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