IP4OS - Unpacking the Possibilities of Intellectual Properties for Open Science
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".