Application of the International Convention on the Elimination of All Forms of Racial Discrimination (Qatar v. United Arab Emirates): So Far, So Good?
Bibliographic record
Abstract
Abstract International law has a long history of dealing with racial discrimination, including its involvement in the perpetration of racial discrimination. However, in establishing a body of norms to tackle the problems of racial discrimination, several multilateral instruments have been adopted under the auspices of the United Nations addressing this malaise to various extents with the most extensive being the International Convention on the Elimination of All Forms of Racial Discrimination ( CERD ) of 21 December 1965. While lauded for its singular and dedicated focus on racial discrimination, the Convention is challenged, at least interpretatively, as to the grounds for racial discrimination within its remit. Events occurring between Qatar and the United Arab Emirates on 5 June 2017 have afforded the International Court of Justice as the principal judicial organ of the United Nations, an opportunity—the third since the coming into effect of the Convention—to interpret this landmark treaty.
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".