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Record W2067667758 · doi:10.1002/meet.14504301124

Authenticity revisited: The cultural implications of a digital reality

2006· article· en· W2067667758 on OpenAlexaff
Bonnie Mak, Heather MacNeil, Jennifer Douglas

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2006
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPanel discussionRendering (computer graphics)SociologyPersonaMedia studiesAestheticsComputer scienceArtAdvertisingHumanitiesBusiness

Abstract

fetched live from OpenAlex

Abstract UNESCO's “Memory of the World” and Google Print are just two of the many projects that seek to digitize our cultural record. The duplication, preservation, and dissemination of these sources with digital technology, however, come at a cost. The panel will explore the cultural implications of transferring and re‐rendering our historical, cultural, and intellectual legacy in a new medium. In addition to exploring the idioms of digitized informational realities, the panel will also investigate how, recursively, these spaces are effecting change in the analogue world. The re‐appearance of historical artifacts as digitized entities, as well as the emergence of born‐digital entities, has forced us to question our traditional ideas about what constitutes an original or a copy, and what we mean by the term ‘authentic’. Inspired by the success of last year's session, “Authenticity: New Personas for Digital Media,” the panel for 2006 will take a more comprehensive look at the concept of authenticity in both analogue and digital environments. This interdisciplinary panel of established and junior scholars from the humanities and the social sciences will suggest new ways of approaching and understanding emergent informational realities.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.365
Teacher spread0.332 · 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 designTheoretical or conceptual
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

Citations0
Published2006
Admission routes1
Has abstractyes

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