The Right to Benefit from Science and Its Implications for Genomic Data Sharing
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.
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 | 0.036 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".