De la « participation inclusive » à la « participation citoyenne », comment le musée Stella Matutina s’ancre-t-il dans la transition territoriale de l’île de La Réunion ?
Bibliographic record
Abstract
It is a fact that the museum today is no longer seen just as a single light in the darkness, a guardian of the temple of human genius, but more as an actor in the society which interacts with it through conservation and presentation for the purpose of knowledge, education and the pleasure of the public. The scientific and cultural project (Scp) has thus become the most powerful and intimate expression of the museal project, drawing on an analysis of the institution’s vocation and environment. Nevertheless, even though the publics are among the target groups they’re aimed at, the mechanisms to meet them still have trouble taking them seriously into account. Certainly, identifying them follows a well-established typology, increasingly diverse and continually renewed. Still, these categories derive more from rationales of communication than from appropriation. Many museums have been able to find a degree of approval by communicating on the reputation of their collections and the attractiveness of their cultural programming; however, there is a negative turn-over of publics over time. Recent events linked to the global COVID-19 pandemic forced them to forge a new relationship with the publics, since events could no longer be staged. Despite all efforts to democratize and to build public loyalty, museums are still restrained in terms of the official manifestation of inclusion in their scientific and cultural projects. No doubt one of the main reasons is that inclusion has not yet assumed its full place. Even if we agree to accept the social role of the museum, its position in the scientific and cultural project remains at the discretion of the writers. This look at the museum’s territorial attachment was developed within the Culture et territoire research program at Île de la Réunion, where inclusive participation was studied from the perspective of the actors’ strategy to understand how certain museums like Stella Matutina interact with their territory and their systemic dimension (social, economic, political, cultural . . .). The analysis also included a study of cultural mediation at Stella Matutina in order to understand how the populations living near the museal institution were involved in their establishment and development within their territory. At the time when the debate asked about inclusion in the political sphere in connection with “citizen participation” and “social justice” (Salguero, 2020), to what extent are museums evolving and participating in the territorial transformation?
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".