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Record W4312177544 · doi:10.18357/otessac.2022.2.1.68

Community-Led Infrastructures for Open Access Books: A Sustainable Model and Platform

2022· article· en· W4312177544 on OpenAlexvenueno aff
Judith Fathallah, Martin Paul Eve, Tom Grady

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Conference · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
FundersResearch EnglandArcadia Fund
KeywordsPublishingRevenueBusiness modelVariety (cybernetics)Promotion (chess)Computer scienceTheme (computing)Revenue modelWorld Wide WebBusinessPolitical scienceMarketingArtificial intelligenceLaw

Abstract

fetched live from OpenAlex

This paper introduces two major outputs of the Community-led Open Publication Infrastructures for Monographs project, the Open Book Collective (OBC) and platform and the Opening the Future publishing model. The OBC, a charitable entity, will host an infrastructure and revenue management platform for the support, access, distribution, and promotion of open access (OA) books beyond models relying on book processing models. I then discuss a revenue model for publishers who wish to flip to an OA model without book processing charges. This ‘Opening the Future’ model has already been successfully implemented by two publishers, the Central European University Press and Liverpool University Press. This paper relates to the conference theme of sustaining positive change. The international move towards OA book publishing must be approached through models that render OA books equitable and accessible to the widest variety of international readers and authors. This necessitates thinking beyond book processing charges and the potential monopolisation of the OA landscape by major publishers, supporting a diversity of approaches in a networked model we call ‘scaling small.’

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.999

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 studies0.0020.000
Scholarly communication0.0040.006
Open science0.0020.004
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 head0.068
GPT teacher head0.348
Teacher spread0.280 · 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.

Study designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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