A third-world critique of the international human rights-based approach to content moderation
Bibliographic record
Abstract
Content moderation by social media platforms, once hailed as the holy grail for stamping out illegal content such as hate speech and disinformation, is failing in the Global South. Global conversations on content moderation and governance have primarily centred around an individualistic conception of human rights, largely dominated by responses grounded on international human rights law (IHRL). While IHRL is a dominant normative framework on content moderation, it often invisiblises (and ignores) the concerns of users in the Global South, mainly in Africa. By analysing insights from Africa and incorporating Third World Approaches to International Law (TWAIL), this Article explores the limitations of IHRL as a governing framework for content in Africa, which stem from its Eurocentric legal foundations, epistemic and linguistic blind spots. The Article ultimately advocates for an African approach to content moderation, anchored in a communal conception of human rights as articulated in African human rights law.
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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 | 0.058 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.106 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| 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".