Blobgects: Digital museum catalogs and diverse user communities
Bibliographic record
Abstract
Abstract This article presents an exploratory study of “Blobgects,” an experimental interface for an online museum catalog that enables social tagging and blogging activity around a set of cultural heritage objects held by a preeminent museum of anthropology and archaeology. This study attempts to understand not just whether social tagging and commenting about these objects is useful but rather whose tags and voices matter in presenting different “expert” perspectives around digital museum objects. Based on an empirical comparison between two different user groups (Canadian Inuit high‐school students and museum studies students in the United States), we found that merely adding the ability to tag and comment to the museum's catalog does not sufficiently allow users to learn about or engage with the objects represented by catalog entries. Rather, the specialist language of the catalog provides too little contextualization for users to enter into the sort of dialog that proponents of Web 2.0 technologies promise. Overall, we propose a more nuanced application of Web 2.0 technologies within museums—one which provides a contextual basis that gives users a starting point for engagement and permits users to make sense of objects in relation to their own needs, uses, and understandings.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".