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Record W2221053130 · doi:10.22230/src.2015v6n3a111

Playing Well With Others: The Social Edition and Computational Collaboration

2015· article· en· W2221053130 on OpenAlex

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueScholarly and Research Communication · 2015
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsOkanagan CollegeUniversity of Victoria
Fundersnot available
KeywordsComputer scienceWorld Wide WebRelation (database)Encoding (memory)Value (mathematics)Data scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This article draws on the Social Edition of the Devonshire Manuscript’s RDFa encoding practice as a case study of how to formalize statements about entities on the Web in a way that is machine-parsable. RDFa encoding allows machines to become collaborators with human readers in the discovery of new connections between entities (people, places, and events) even between websites. The edition’s encoding is motivated by the INKE Modelling and Prototyping team’s guiding research question about the implications and impact of real-time applications in relation to traditionally static knowledge objects. The authors argue for the value of bringing texts into communication with other texts, through RDFa, allowing virtual collaboration even when the scholars behind the projects do not know one another.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly 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 score0.531
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.002
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 head0.105
GPT teacher head0.379
Teacher spread0.274 · 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