International human rights: an example of global norms diffusion?
Bibliographic record
Abstract
On 2 February 2009, the United Nations had to deal with experiences of discrimination against intersex people.Reportedly, this was the first time that this theme made it into an official UN committee.The issue came up in an "alternative report" to the UN Committee on the Elimination of All Forms of Discrimination Against Women, put together by "XY Women", a civil society organization based in Germany.Shortly afterwards, the acronym "LGBT" (= lesbians, gays, bisexuals and trans-people) which had become customary long before, was amended by including an I (for intersex), thus leading to LGBTI.This change can actually be observed at a global scale.Human rights experts from Canada to South Africa, or from Austria to Bangladesh are using the same technical language, including numerous (maybe too many) acronyms, like LGBTI.In that sense, norm diffusion is an unquestionable reality.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".