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Record W4210466302 · doi:10.24908/iee.2021.14.3.f

Transformative choices towards a sustainable academic publishing system

2022· article· en· W4210466302 on OpenAlexvenueno aff
Mohsen Kayal, Jane Ballard, Ehsan Kayal

Bibliographic record

VenueIdeas in Ecology and Evolution · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingTransformative learningPublic relationsScientific publishingScientific communicationScientific progressPolitical scienceSustainable developmentSociologyEngineering ethicsLawLibrary scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Communicating new scientific discoveries is key to human progress. Yet, this endeavor has been increasingly hindered by monetary restrictions that restrain scientists from publishing their findings and accessing other scientists’ reports. This process is further exacerbated by a large portion of publishing media owned by private companies that, in contrast with journals from scientific societies, do not reinject academic publishing benefits into the scientific community. As the academic world is not exempt from economic crises and funding restrictions, new alternatives are necessary to support a fair and economically sustainable publishing system for scientists and society as a whole. After summarizing major shortcomings of academic publishing today, we present several solutions that span the levels of the individual scientist, the scientific community, and the publisher to initiate a transformative change towards more sustainable scientific publishing. By providing a voice to the many scientists who are fundamental protagonists, yet often powerless witnesses, of the academic publishing system, as well as a roadmap for implementing solutions, we hope this initiative will go beyond sparking increased awareness and promote a shift towards more sustainable scientific publishing practices.

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 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)0.007
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.367
Teacher spread0.329 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

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