A Contested Ethnic Tourism Asset: The Case of Matonge in Brussels
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".