Constructing Local Belonging through Art and Activism in Context of Anti-Migration Politics, Stigmatisation and Gentrification: What Migration Studies can Learn from Belleville and Maddalena
Bibliographic record
Abstract
Despite a decade of self-criticism, research perspectives on migration studies remain too often centred on national belonging (Glick Schiller & Çağlar 2011). Based on two empirical examples, self-organised fashion and music shows in Paris and Genoa, this article shows how “event lenses” can constructively replace “ethnic lenses” in the analysis of artivistic practices that aim at changing political situations and living conditions. Wearing “event lenses” also helps us to question supposed homogeneities and to investigate common civic or political practices and interests by emphasizing multiple belonging processes in various social situations (Yuval-Davis et al. 2006, 7). I show how the research perspective of migration studies can be guided by the complexity of migrants’ multiple belongings and by situational analysis. The article presents results from my ERC project “ARTIVISM. Art and activism. Creativity and Performance as Subversive Forms of Political Expression in Super-Diverse Cities”, guided by an event-centred approach and multi-sensory audio-visual ethnography. The Parisian district of Belleville and the Maddalena district of Genoa suffer both from negative stigmatisations related to informal economical practices. I show how the super-diverse populations in these marginalised but gentrifying spaces creatively reverse xenophobic stigmata, by valorising their biographies and multiple belongings through fashion shows.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.044 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".