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Record W2149831993 · doi:10.1111/muan.12088

Devil in The Digital: Ambivalent Results in an Object‐Based Teaching Course

2015· article· en· W2149831993 on OpenAlex
Mark Turin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMuseum Anthropology · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContextualizationDigitizationClass (philosophy)MetadataSet (abstract data type)Capstone courseComputer scienceObject (grammar)LegitimacyWorld Wide WebCourse (navigation)MultimediaMathematics educationSociologyCurriculumPedagogyEngineeringPsychologyPolitical science

Abstract

fetched live from OpenAlex

Abstract In 2013, I piloted a course in which students used Web‐based tools to explore underdocumented collections of Himalayan materials at Yale University. Through class‐based research and contextualization, I set students the goal of augmenting existing metadata and designing media‐rich, virtual tours of the collections that could be incorporated into the sparse catalogue holdings held within the library system. The process was experimental and had mixed results, as this article documents. The class provided an opportunity for undergraduate students from any discipline to work with objects and primary materials, requiring them to evaluate different sources of information, value, and legitimacy. Learning outcomes were nontraditional and intentionally underscripted. The collaborative and hands‐on approaches toward digitization that de‐emphasized the authority of the instructor were unsettling to some students. [digital humanities, mobile classroom, critical pedagogy, material culture, Himalaya]

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.

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)0.001
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 score0.656
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.091
GPT teacher head0.325
Teacher spread0.234 · 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