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Record W2180645625 · doi:10.22230/src.2015v6n2a232

From Technical Standards to Research Communities: Implementing New Knowledge Environments Gatherings, Sydney 2014 and Whistler 2015

2015· article· en· W2180645625 on OpenAlexaffvenueabout
Alyssa Arbuckle, Aaron Mauro, Lynne Siemens

Bibliographic record

VenueScholarly and Research Communication · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAffordanceConversationLibrary scienceReading (process)Research councilSociologyKnowledge productionMedia studiesPublic relationsPolitical scienceComputer scienceKnowledge managementGovernment (linguistics)

Abstract

fetched live from OpenAlex

On December 8, 2014, researchers, students, librarians, and other participants gathered together in Sydney, Australia at the State Library of New South Wales for the 7th annual Implementing New Knowledge Environments (INKE) Birds-of-a-Feather conference, “Research Foundations for Understanding Books and Reading in the Digital Age.” On January 27 and 28, 2015, a similar group of stakeholders met in Whistler, BC, Canada, at the Nita Lake Lodge for the second year to discuss “Sustaining Partnerships to Transform Scholarly Production.” The events were hosted by INKE and sponsored by the Social Sciences and Humanities Research Council (SSHRC). Drawing from these two gatherings, the articles collected in this latest issue of Scholarly and Research Communication reflect an ongoing conversation in SRC (see 5.4), on new ways humanities researchers, publishers, and policy makers can collaborate effectively to make the most of the new affordances of computational tools and 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)0.014
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.284
GPT teacher head0.432
Teacher spread0.148 · 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 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

Citations0
Published2015
Admission routes3
Has abstractyes

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