Bibliographic record
Abstract
The museum acts as an intermediary between history, traditions, knowledge, and man. At the same time, the museum forms a certain type of thinking. The article suggests that the museum can also be considered as a medium (in the context of the tradition of the Toronto School of Communication). The article is devoted to the consistent comparison of the museum, especially its modern form, and the media. Previously, the museum was not considered as a medium. The article presents the conceptual framework of the study. The forms of classical and modern museums are compared: the class museum is equated by the author with traditional media, while the modern one is correlated with new types of media. Among such media characteristics, it is proposed to distinguish: the transmission of information to the target audience; the regularity of broadcasting information; rubrication and orientation to the needs of the target audience. Based on the research of Western media researchers, J. Bolter and R. Gruzin, the media characteristics considered in relation to museum activities are remediation, hypermedia and immediacy. The author gives examples of exhibitions of the Pushkin State Museum of Fine Arts, the State Tretyakov Gallery, the HPP-2 exhibition space, as well as the online Google Art&Culture platform.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".