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Record W7106369509 · doi:10.5281/zenodo.17675510

IP4OS - Unpacking the Possibilities of Intellectual Properties for Open Science

2025· article· W7106369509 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Language
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsMiller Group (Canada)
FundersEuropean Commission
KeywordsIntellectual propertyUnpackingCitizen journalismResponsible Research and InnovationKnowledge sharingParticipatory action researchWork (physics)Project management

Abstract

fetched live from OpenAlex

IP4OS is a project funded under Horizon Europe (grant agreement No. 101188026). With a strong consortium of eight partners, the project seeks to revolutionise and strengthen research knowledge valorisation by promoting a coordinated approach between intellectual property (IP) management and open science (OS) practices. The project supports research institutions and researchers in understanding the full value(s) of their project outputs and in identifying the most suitable strategies to maximise impact—whether through tailored IP approaches, OS practices, or a thoughtful combination of both to create economic, societal, and/or environmental benefits. Through this integrated approach, the project connects flexible and context-appropriate IP tools with the open sharing of FAIR research outputs to enable more effective and responsible valorisation. Through it's evidence-based analysis of IP and OS interplay, engaging campaign activities and participatory training programme IP4OS establishes a Community of Practice that is empowered to boost Europe's R&I eco-system through IP and OS. This proposal describes IP4OS's activities and project goals.

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)0.012
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Scholarly communication, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0100.003
Scholarly communication0.0460.024
Open science0.0320.049
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.157
GPT teacher head0.345
Teacher spread0.188 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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