Turning the World Inside Out: Situating JR’s Wish within Cultures of Participation
Bibliographic record
Abstract
En 2011, l’artiste de rue et photographe JR lance un défi à l’auditoire de la conférence TED : l’aider à la réalisation de son projet Inside Out en participant à des projets de montage de portraits publics qui viseront à générer un dialogue, susciter la sensibilisation, raconter des histoires ou rendre les communautés visibles. Trois ans plus tard, avec plus de 196,500 portraits affichés dans le monde entier, 902 actions de groupe et un flot constant de nouveaux projets publiés sur le site web et la page Facebook de Inside Out, ce phénomène mondial continue de croître. Cet article situe Inside Out parmi les cultures de participation qui ont émergé depuis les années 1990. De plus, il explique comment l’impact et les conséquences du néolibéralisme mondial ont contribué à établir les conditions dans lesquelles les actions de groupe de Inside Out peuvent se développer.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.035 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".