Bibliographic record
Abstract
As cultural institutions once founded on privacy, protocol and practice, museums must now choose how best to navigate the transparency presented by social media including Facebook, YouTube, Flickr and Twitter.When the Ontario based organization Archives and Museum Informatics held its first "Museums and the Web" conference in 1997, nascent concerns emphasized the frame rather than the function of social media -who will use the Internet rather than how.Over the last twelve years, major museums such as New York's Museum of Modern Art have evolved from a static web presence to the cultivation of a participatory museum culture through the skillful implementation of social media.The Australian Museum is conducting an online blog experiment to determine if they are able to engage their audience in exhibition development.Social media is a participatory platform fortified by freedom of expression.This platform can alternate between pedestal and soapbox as users are given a public forum for personal ideologies.Though public in nature, museums are notoriously private in practice.Logic suggests that such a lack of transparency leads easily to a disconnect from constituents and hinders the development of a community base.Engagement in social media revives the original conception of museum as forum.This research project examines the issues surrounding the shifting discourse between museum and patron and the impact of social media on the development of a museum community.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".