Bibliographic record
Abstract
In 1935, Librarian John E. Abbott of the Museum of Modern Art wrote this of the contemporary status of film preservation: “the situation is as though there existed a great interest in painting on the part of the public, but that almost no painting were ever exhibited save those executed within the previous twelve months.” In the early twentieth century, film collections were not sought after by museums, because the relevance of film to museum mandates had not yet been defined. In this paper, we refer to the creation of some of the first museum film libraries and archives, in order to examine the effort of their establishment within a museum, and the philosophical challenges and appeals that must be addressed when these mediums meet, in the interplay between archival and museological theory. We shall briefly review the beginning of film museums, and then discuss where the nature and priorities of museums most affected these pioneering film libraries and archives. These influences manifest in the rationale of why films should be collected, in the details of what should be acquired, and in the practical and philosophical challenges that are not commonly found in other information institutions, but are characteristic of museum work.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".