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Record W3129446086 · doi:10.1108/lhtn-11-2020-0106

ORCID education: a departmental approach

2021· article· en· W3129446086 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.

Bibliographic record

VenueLibrary Hi Tech News · 2021
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOutreachContext (archaeology)Promotion (chess)Library scienceComputer sciencePolitical scienceHistory

Abstract

fetched live from OpenAlex

Purpose This paper aims to provide a case study of an ORCID promotion at the University of Waterloo School of Optometry and Vision Science, providing context for the importance of education in ORCID outreach. Design/methodology/approach The three-month ORCID promotion used workshops and individual appointments to educate faculty about ORCID, identity management systems and research impact and scholarly communications. Findings A targeted and personal approach to ORCID promotion focused on education about why you might use this author disambiguation system resulted in 80% of the faculty within the School of Optometry and Vision Science signing up for, or using ORCID. Scaling an ORCID implementation to a larger group would likely benefit from a dedicated project group, and integration with existing institutional systems such as a requirement of an ORCID for internal grant applications. Originality/value Although time consuming, this small-scale ORCID promotion with one department reveals that a departmental approach to ORCID education may lead to larger conversations about scholarly communications and a stronger relationship between faculty and the library.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesBibliometrics, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.498
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0150.150
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.462
GPT teacher head0.538
Teacher spread0.076 · 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