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Record W2913175269 · doi:10.1002/pra2.2018.14505501126

Digital humanities, libraries, and crowdsourcing: Foundations of digital textual technologies

2018· article· en· W2913175269 on OpenAlexaff
Twyla Gibson, Stuart J. Murray, Sanda Erdelez, Bridget A. Disney, Brian Greenspan

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsCarleton University
Fundersnot available
KeywordsDigital humanitiesCrowdsourcingKey (lock)Digital collectionsInterpretation (philosophy)Presentation (obstetrics)Citizen journalismWorld Wide WebLibrary scienceData scienceComputer scienceSociology

Abstract

fetched live from OpenAlex

ABSTRACT The Greek Key is a prototype Virtual Research Environment (VRE) for the analysis of patterns in ancient texts and manuscripts. A Powerpoint presentation demonstrates how the VRE functions through a comparative case study of the organization of information in Plato and Aristotle. The interdisciplinary method combines historical, philological, and philosophical techniques with approaches from the digital humanities. The Greek Key is a collaborative, scalable, multidisciplinary project that has the potential to engage librarians in participatory strategies such as crowdsourcing (or “Citizen Science”). The Greek Key tools for visualizing textual data have the potential to: reveal previously undetected patterns in books and collections; make connections among different works; help users bring new information to bear on interpretation; demonstrate the significance of findings; and generate fresh insights about works of literature that have had a central and enduring influence on both our scholarly traditions and the history of libraries and librarianship. The VRE will make it possible for academic researchers and librarians to pursue perennial questions in innovative ways, and respond to questions that do not lend themselves to more traditional methods.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
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 score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0020.010
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 head0.026
GPT teacher head0.224
Teacher spread0.198 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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