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Low-Key Load-Bearing

2025· article· en· W4408927653 on OpenAlexaffvenueabout
Sonya Betz, Emma Uhl, Mike Nason

Bibliographic record

VenueCanadian Journal of Information and Library Science · 2025
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsAlberta LibraryUniversity of Alberta
Fundersnot available
KeywordsKey (lock)Bearing (navigation)Load bearingComputer scienceEngineeringStructural engineeringComputer securityArtificial intelligence

Abstract

fetched live from OpenAlex

Canadian academic libraries play a demonstrably load-bearing yet under-recognized role in the nation’s scholarly publishing ecosystem, supporting a predominantly independent, non-commercial journal landscape. While global trends reveal an oligopoly of large commercial publishers, Canadian publishing remains defined by diversity, with libraries offering essential infrastructure and services for journals rooted in open access and equity. This study explores the scope and nature of library publishing services in Canada, analyzing data from 42 institutions to reveal significant contributions to the production, dissemination, and preservation of scholarly knowledge. Despite limited resources, these programs provide critical support for diamond open access publishing models, bibliodiversity, and underserved journal types. As national and international conversations on scholarly publishing evolve, it is vital to recognize libraries not just as contributors, but as key players driving a more equitable and sustainable publishing system.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.466

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0330.005

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 head0.002
GPT teacher head0.158
Teacher spread0.155 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2025
Admission routes3
Has abstractyes

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