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Record W3174830621 · doi:10.5281/zenodo.5017704

Future of Scholarly Communication . Forging an inclusive and innovative research infrastructure for scholarly communication in Social Sciences and Humanities

2021· article· en· W3174830621 on OpenAlexaff
Karla Avanço, Ana Balula, Marta Błaszczyńska, Anna M. Buchner, Lorena Caliman, Claire Clivaz, Carlos Costa, Mateusz Franczak, Rupert Gatti, Elena Giglia, Arnaud Gingold, Susana Jarmelo, Maria João Padez, Delfim F. Leão, Maciej Maryl, Iva Melinščak Zlodi, Kajetan Mojsak, Agata Morka, Tom Mosterd, Elisa Nury, Cornelia Plag, Valérie Schäfer, Mickael Silva, Jadranka Stojanovski, Bartłomiej Szleszyński, Agnieszka Szulińska, Erzsébet Tóth-Czifra, Piotr Wciślik, Lars Wieneke

Bibliographic record

VenueInstitutional Research Information System University of Turin (University of Turin) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsCanadian Nautical Research Society
FundersEuropean Commission
KeywordsScholarly communicationForgingSociologyDigital humanitiesLibrary scienceEngineeringPolitical scienceEngineering ethicsMedia studiesComputer scienceMechanical engineeringPublishing

Abstract

fetched live from OpenAlex

This report discusses the scholarly communication issues in Social Sciences and Humanities that are relevant to the future development and functioning of OPERAS. The outcomes collected here can be divided into two groups of innovations regarding 1) the operation of OPERAS, and 2) its activities. The “operational” issues include the ways in which an innovative research infrastructure should be governed (Chapter 1) as well as the business models for open access publications in Social Sciences and Humanities (Chapter 2). The other group of issues is dedicated to strategic areas where OPERAS and its services may play an instrumental role in providing, enabling, or unlocking innovation: FAIR data (Chapter 3), bibliodiversity and multilingualism in scholarly communication (Chapter 4), the future of scholarly writing (Chapter 5), and quality assessment (Chapter 6). Each chapter provides an overview of the main findings and challenges with emphasis on recommendations for OPERAS and other stakeholders like e-infrastructures, publishers, SSH researchers, research performing organisations, policy makers, and funders. Links to data and further publications stemming from work concerning particular tasks are located at the end of each chapter.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.003
Scholarly communication0.0000.012
Open science0.0010.000
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 head0.105
GPT teacher head0.386
Teacher spread0.282 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations1
Published2021
Admission routes1
Has abstractyes

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