The Right to Benefit from Science and Its Implications for Genomic Data Sharing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The right to benefit from science and its applications is one of the least studied, discussed and applied human rights. In the current time of globalization, characterized by the rapid advancement of science and its technological applications, as well as by increased flows of scientific data, there is a growing need to fully awaken the right of everyone to enjoy the benefits of science. This would enable science to better serve the humanitarian purposes of the law as well as foster scientific and technological development through data sharing. This article contributes to the awakening of the right by exploring it doctrinally with the aim of ascertaining its normative content by reference to the preparatory works of Article 15 of the International Covenant on Economic, Social and Cultural Rights and, especially, the subsequent state practice in its application. Based on the evidence, it will be argued that, today, the right to benefit from science has two aspects – first, the right to access scientific knowledge and information and, second, the right to benefit from scientific applications. It will be shown that the first aspect of the right is increasingly reflected in the practice of states and international organizations and has important implications for the regulation and sharing of big genomic data.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it