Bibliographic record
Abstract
In the globalization process many cultural traditions around the world tend to disappear under the pressure of standardisation of practice and content. Cultural diversity seems to recede more and more. In a proactive position, UNESCO made a universal declaration on cultural diversity in 2001 that it would aim at heritage preservation.In the same effort of protection and enhancement of cultural diversity, museums are developing Internet material to preserve and disseminate cultural knowledge and heritage and to create interactive experiences between users and content. This has given birth to what some refer to as cybermuseology. But one can ask, do virtual museums present more than images of objects? Can the knowledge of localised cultural heritage and practices be transferred without losing the context it stems from, or what de B’béri (Cinema andSocial Discourse 64) defines as “the condition under which a society produces specific meaning”? More specifically, can information and communication technologies (ICT) transfer tacit knowledge, human experience, and tangible cultural heritage, and if so, what can we learn through this new process of cultural codification?This paper shall focus on explaining cybermuseology and then explore the process of knowledge codification and the links we can draw with heritage codification. In the last section I will discuss virtual experiences and try to determine how museums are using the virtual to protect and promote cultural diversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".