Bibliographic record
Abstract
“Non-public” was used for the first time in May, 1968, by those working professionally in the cultural domain in France. At the time, they were gathered in Villeurbanne at the head office of the TNP (French National Popular Theatres), and they used this notion in a very militant way to describe all those who were excluded from culture, and whom they considered to have a fundamental right to all cultural offers. In this book, nine researchers from France, Québec and Mexico tackle these questions through both qualitative and quantitative contributions dealing with various cultural sectors in which the question of non-publics remains unanswered. In fact, the non-public is not so much a group of non-participants but individuals blatantly incapable of appreciating a culture that is unfamiliar, even foreign. For over a century, the popular education movement, in its initial project to bring public and culture closer together, has emphasized this cultural gap, which even today, justifies the necessity for cultural mediation policies. The near-militant voluntarism of the active players in cultural mediation engenders certain expectations: after a large investment in cultural creation is it not justifiable to aspire to reach the largest possible audience?
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 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".