Bibliographic record
Abstract
Museums face numerous challenges in the 21st century. Among these are a loss of cultural authority and the dispersion of collected objects through museums worldwide that makes it impossible for users to know where to search, or how to search, for items that might be of interest to them. The consequences are that museums and their holdings are less well known, and less understood, than they ought to be. An emerging technical infrastructure of “smart” objects and location-aware devices can play a role in enabling museums to succeed in these tasks. If the museum adds geographical coordinates to the description of the objects in its collections, people who are in the vicinity of those locations can be informed about the holdings of the (distant) museum, 24 hrs a day. These people include those from whose cultures the objects were once taken and people visiting as tourists; these two audiences are especially interested in understanding the museum’s collection, because it is relevant to them, literally ‘where they stand.’ Having access to the cultural objects that have been removed from their original contexts can reduce demands that they be repatriated, especially if the museum can engage locals to contribute their knowledge of the objects, and tourists to supply terms in their native language that would help their compatriots find the object. In this way, geo-aware objects could help museum fulfill numerous demands currently being made of them and usher in an extra-institutional dimension to cultural interpretation. This chapter examines the requirements for museum success in a geo-aware future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".