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Record W7015462398

SUPPLEMENTAL MATERIALS: Factors influencing Canadian HASS researchers’ open access publishing practices: Implication for the future of scholarly communication

2022· article· en· W7015462398 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
Fundersnot available
KeywordsScholarly communicationPublishingAccess to informationElectronic publishingInformation Dissemination
DOInot available

Abstract

fetched live from OpenAlex

This is a structured questionnaire that was employed to investigate open access (OA) publishing practices of 228 researchers in Humanities, Arts and Social Sciences disciplines in Canada.Researchers' OA publishing practices was operationalized using three factors: Experience, Frequency and Extent.The independent variables comprise three factors; Visibility, Prestige and Altruism.These factors represent self-reported benefits of publishing in OA outlets, adapted from existing studies.Validity of items adapted in this study was re-confirmed through exploratory factor analysis (i.e., principal components analysis with varimax rotation), with results exceeding the acceptable limit of .55 (Comrey & Lee, 1992).Similarly, reliability analysis of the scales showed a high Cronbach's  exceeding the minimally acceptable range of 0.65-0.70(DeVellis, 1991).This validated questionnaire can be used for investigating OA publishing practices of faculty, researchers, librarians, students, and other user categories in various context.Appendix A: Survey questionnaire 1.

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)0.004
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.998
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.078
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.015
Science and technology studies0.0060.001
Scholarly communication0.0050.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.6040.058

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 head0.275
GPT teacher head0.454
Teacher spread0.179 · 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
GenreDataset

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".

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Citations0
Published2022
Admission routes1
Has abstractno

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