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Record W2587239300 · doi:10.5281/zenodo.260101

European Survey On Scholarly Practices And Digital Needs In The Arts And Humanities - Highlights Report

2017· preprint· en· W2587239300 on OpenAlexaff
Costis Dallas, Nephelie Chatzidiakou, Agiatis Benardou, Claire Clivaz, John Cunningham, Meredith Dabek, Patricia Garrido, Elena González‐Blanco, Jurij Hadalin, Lorna Hughes, Beat Immenhauser, Anne Joly, Ingrida Kelpšienė, Koraljka Kuzman Šlogar, Marko Lukin, Irena Marinski, Maciej Maryl, Robert Owain, Eliza Papaki, Gerlinde Schneider, Walter Scholger, Susan Schreibman, Zoe Schubert, Toma Tasovac, Manfred Thaller, Piotr Wciślik, Marcin Werla, Tvrtko Zebec

Bibliographic record

VenueENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · 2017
Typepreprint
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDigital humanitiesThe artsObservatoryLibrary scienceSociologyGeographyPublic relationsPolitical scienceVisual artsComputer scienceArt

Abstract

fetched live from OpenAlex

This report summarizes the statistical analysis of the findings of a web-based survey conducted by the Digital Methods and Practices Observatory (DiMPO), a working group under VCC2 of the DARIAH research infrastructure (Digital Research Infrastructure for the Arts and Humanities). In order to provide an evidence-based, up-to-date, and meaningful account of the emerging information practices, needs and attitudes of arts and humanities researchers in the evolving European digital scholarly environment, the web survey involved a transnational team of researchers from more than a dozen countries, and addressed digitally-enabled research practices, attitudes and needs in all areas of Europe and across different arts and humanities disciplines and contexts.

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)0.008
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.010
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.003

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 head0.128
GPT teacher head0.288
Teacher spread0.161 · 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.

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

Citations8
Published2017
Admission routes1
Has abstractyes

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