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Record W4388579940 · doi:10.15291/pubmet.3924

Bringing efficiencies to tens of thousands of journals

2022· article· en· W4388579940 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

VenuePUBMET · 2022
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPresentation (obstetrics)Computer scienceOpen source softwareOpen sourceKey (lock)SoftwareWorld Wide WebData scienceComputer securityOperating system

Abstract

fetched live from OpenAlex

In addition to the growing number of scholarly journals published by the so-called “big five”, there are tens of thousands of journals that are published by individual scholars or by academic institutions. These smaller operations are a source of great bibliodiversity that deserves to be encouraged but can also be seen as inefficiencies in the system as a whole. The use of a common software—Open Journal Systems (OJS)—is helping these journals take advantage of an economy of scale without needing to centralize or homogenize them. The key to promoting both efficiency and bibliodiversity is in OJS’s open source nature. This presentation will describe the ways in which PKP’s open source software is bringing efficacy to journal operations, to the discovery of their content, and, in the best of cases, to supporting a transformation of the system as a whole.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.112
GPT teacher head0.361
Teacher spread0.249 · 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