D.5.3 Recommendations for data sovereignty of consumers, engagement processes in development phase, tools for data based systems and data sharing processes
Bibliographic record
Abstract
This deliverable (and the attached complementary Enabling Environments for Data Privacy and Innovation brochure) outlines how digital technologies can advance food and nutrition systems while safeguarding user rights and trust. Developed within the FOODITY project, this it provides practical recommendations across four areas: data sovereignty and trust, inclusive app development, responsible data-based nutrition systems, and policy actions. It emphasises privacy-by-design, transparent consent, interoperable infrastructures, and inclusive co-creation to ensure digital tools are both ethical and effective. Designed for developers, innovators, and policymakers, this deliverable and brochure (developed to provide readers a more visual-friendly version) offers a roadmap for aligning digital innovation with legal, ethical, and social values. By fostering user control, accountability, and inclusivity, it aims to create digital environments where data drives meaningful progress without compromising individual rights.
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 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 | 0.033 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.133 | 0.102 |
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".