Ce que l'art fait à la ville au Moyen-Orient et au Maghreb: . Pratiques artistiques, expressions du politique et transformations de l’espace public
Bibliographic record
Abstract
The fourth issue of Manazir Journal focuses on “making art” in urban public space in North Africa and the Middle East. Whether visual or performative, these urban arts (tags, graffiti, street art, murals, performances, live shows, sculptures and installations) contribute to renewing forms of expression of politics in public space and, more broadly, to transforming urban space. Indeed, the graffiti and street art scene has accelerated over the past decade in line with the so-called “Arab Spring”: every year, graffiti on walls multiply, new urban art centers appear, and festivals entirely dedicated to street art are organized. All these initiatives are gradually turning the city into an “open-air gallery” and are transforming the relationship between city dwellers and urban public space. While many scholarly works have begun to take an interest in artistic practices in the MENA region, few of them have explored their urban dimensions. By asking what art does to the city, the contributions in this issue question the renewal of the links between art, modes of appropriation of urban space, and forms of political expression.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".