Bibliographic record
Abstract
Abstract The rapid transformation of museums over the last twenty years, both in Canada and around the world, has provoked numerous commentaries and interpretations. It has also fanned the flames of an argument that began three hundred years ago. The quarrel of the ancients and the moderns on the question of “museumfication” continues today. The quarrel is now not so much about problem of works and objects being placed in a kind of thesaurus, removed from their true context and accessible to only a limited public, but rather about the mummification of living traditions, intangible heritage, public spaces, and certain cities or their neighborhoods. The museum networks is growing extremely quickly, while at the same time becoming part of today's mass media universe. These changes are received enthusiastically by some, but are met with disapproval by others. Communication, theatrical presentation, the exchange and sharing of works, and the increased forging of links between institutions throughout the world have all contributed to making museums places of encounter and debate. As a result, museums now are among the liveliest and most productive cultural industries in the Western world. This hypermediatization has also brought about internal changes in museums. The goal of the museum has not necessarily been modified, but the ways of managing the institution have definitely undergone a transformation. What is the place of historians and researchers in this shifting world? Are they still welcome there?
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.032 | 0.013 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 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".