Bibliographic record
Abstract
Social media platforms provide opportunities for individuals to share information and express views through social media and other communication platforms. In recent years, there has been a surge in concern about the human rights impacts of ICT companies, including social media providers. The BHR regime, as well as its global applicability regardless of the national jurisdictions of actors and victims, provides the regime with important potential in regard to social media companies. We contribute to remedying that gap by focusing on human rights due diligence in relation to the posting and re-posting of photos of individuals that social media enables. We outline the key features of the UNGPs related to how business enterprises should identify and manage harmful human rights impact and engage with their business relations to do so. We provide examples from case law and social media usage reports on posting, deleting, and deleting photos and outline the features of social media provider business models that differ from those in sectors typi-cally associated with business-related human rights infringements. We discuss implications for the relationship between a social media company and its users and draw conclusions about the potential of the BHR.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".