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Record W3207999717 · doi:10.5281/zenodo.2662490

PARTHENOS D3.3 Foresight Study and Interdisciplinary Research Agenda

2019· article· en· W3207999717 on OpenAlexaff
Mark Hedges, David C. Stuart, Sheena Bassett, Vicky Garnett, Roberta Giacomi, Maurizio Sanesi

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsPrairie Improvement Network
FundersEuropean Commission
KeywordsFutures studiesPolitical scienceEngineering ethicsSociologySocial scienceEnvironmental ethicsEngineeringComputer sciencePhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

In recent years there has been rapid growth both in the development of digital methods and tools and in their application across a wide range of disciplines within humanities and cultural heritage studies. The future development of this landscape depends on a complex and dynamic ecosystem of interactions between a range of factors: changing scholarly priorities, questions and methods; technological advances and new tool development; and the broader social, cultural and economic contexts within which both scholars and infrastructures are situated. This foresight study investigates how digital research methods, technologies and infrastructures in digital humanities and cultural heritage may develop over the next 5-10 years, and provides some recommendations for future interventions to optimize this development. For a summary see: <strong>PARTHENOS Foresight - Executive Summary</strong> (DOI: 10.5281/zenodo.3460653). All PARTHENOS deliverables are available at: http://www.parthenos-project.eu/resources/projects-deliverables

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.572
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0430.010

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 head0.121
GPT teacher head0.294
Teacher spread0.173 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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