Human rights responsibilities in the digital age states, companies and individuals
Bibliographic record
Abstract
Introduction / (Frďřic Bernard and Jean-Henry Morin, University of Geneva) -- I.Framing the debate 1.Digital Responsibility: a Multi-Stakeholder Challenge (Jean-Henry Morin, University of Geneva) -- 2. Who Cares about Privacy? Data as Unpaid Labour (Maurizio Ferraris, University of Turin) -- II.State responsibilities -- 3. Data Protection in Europe and Beyond (Sophie Kwasny, Council of Europe) -- 4. Perils of Data-Intensive Systems in the Philippines and Asia (Jamael A. Jacob, Data Protection Office of the Ateneo de Manila University and Foundation for Media Alternatives) -- 5. Harmful Effects of Artificial Intelligence and Automation (Marwa Fatafta, Transparency International) -- 6. Cybersecurity and Human Rights: Probing the Relationship (Devony Schmidt, Harvard, Vivek Krishnamurthy, University of Ottawa, and Amy Lehr, Center for Strategic and International Studies) III.Company responsibilities -- 7. Freedom of Peaceful Assembly and Association in the Digital Age (Jonathan Andrew, Geneva Academy of International Humanitarian Law and Human Rights) -- 8. Freedom to think and to hold a political opinion: digital threats to political participation in liberal democracies (Jřm̥e Duberry, University of Geneva) -- 9. Governing harmful speech online (Frďřic Bernard, University of Geneva, and Viera Pejchal, United Nations) IV.Individual responsibilities -- 10. Strategies for the Media against Hate Speech (Guido Keel, Zurich University of Applied Sciences) -- 11.Big Data and Citizen-Generated Data for Gender Equality and Health (Claudia Abreu Lopes, UN University, and Marcus Erridge, University of Coimbra) -- 12. The Impact of Digital Technologies on the Rights of the Child (Elizabeth Milovidov, Children's Rights Division, Council of Europe) -- Conclusion / (Frďřic Bernard and Jean-Henry Morin, University of Geneva)
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".