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Record W1866505218 · doi:10.47678/cjhe.v39i3.472

University Supports for Open Access: A Canadian National Survey

2010· article· en· W1866505218 on OpenAlexaffvenueabout
Devon Greyson, Kumiko Vézina, Heather Morrison, Donald Taylor, Charlyn Black

Bibliographic record

VenueCanadian Journal of Higher Education · 2010
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsConcordia UniversityUniversity of British Columbia
Fundersnot available
KeywordsMandatePublic relationsWork (physics)Administration (probate law)Scholarly communicationBusinessPolitical scienceSociologyPublishingEngineering

Abstract

fetched live from OpenAlex

The advent of policies at research-funding organizations requiring grantees to make their funded research openly accessible alters the life cycle of scholarly research. This survey-based study explores the approaches that libraries and research administration offices at the major Canadian universities are employing to support the research-production cycle in an open access era and, in particular, to support researcher adherence to funder open-access requirements. Responses from 21 universities indicated that librarians feel a strong sense of mandate to carry out open access-related activities and provide research supports, while research administrators have a lower sense of mandate and awareness and instead focus largely on assisting researchers with securing grant funding. Canadian research universities already contain infrastructure that could be leveraged to support open access, but maximizing these opportunities requires that research administration offices and university libraries work together more synergistically than they have done traditionally.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.019
Science and technology studies0.0080.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.001
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 head0.651
GPT teacher head0.613
Teacher spread0.038 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainEvaluation
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

Citations24
Published2010
Admission routes3
Has abstractyes

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