MétaCan
Menu
Back to cohort
Record W4255984135 · doi:10.1002/asi.21027

Blobgects: Digital museum catalogs and diverse user communities

2009· article· en· W4255984135 on OpenAlexaboutno aff
Ramesh Srinivasan, Robin Boast, Katherine Becvar, Jonathan Furner

Bibliographic record

VenueJournal of the American Society for Information Science and Technology · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsDialog boxContextualizationWorld Wide WebPoint (geometry)Set (abstract data type)Museum informaticsComputer scienceCultural heritageRelation (database)MuseologyVisual artsHistoryArtInterpretation (philosophy)Archaeology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.237
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society for Information Science and TechnologySame topicMuseums and Cultural HeritageFrench-language works237,207