Dataset on Prohibited Grounds of Discrimination in International Norms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This dataset systematizes the prohibited grounds of discrimination across international anti-discrimination norms. It begins with the Universal Declaration of Human Rights as a foundational reference point and incorporates the grounds of discrimination articulated in major international treaties, including the International Covenant on Civil and Political Rights (ICCPR) and the International Covenant on Economic, Social and Cultural Rights (ICESCR). The dataset further surveys constitutional and statutory provisions from a range of jurisdictions, including the United States, the United Kingdom, Canada, the European Union, France, Germany, the Republic of Korea, and Japan. For the United States, the analysis extends beyond the federal level to include state-level legislation, with particular focus on California and New York. Data collection was conducted as of August 30, 2025, which serves as the reference date for this dataset. This dataset is intended to serve as a foundational resource for scholars engaged in comparative and international studies of anti-discrimination norms. 이 데이터셋은 국제 차별금지 규범에 제시된 차별금지사유를 체계적으로 정리한 것이다. 우선 세계인권선언의 차별금지사유를 출발점으로 삼아, 국제규약(예: 시민적·정치적 권리에 관한 국제규약, 경제적·사회적·문화적 권리에 관한 국제규약 등)에서 규정한 차별금지사유를 포함하였다. 이어서 미국, 영국, 캐나다와 같은 영어권 국가뿐 아니라, 유럽연합(EU), 프랑스, 독일, 한국, 일본의 헌법과 법률에서 규정된 차별금지사유를 조사하였다. 특히 미국의 경우에는 연방 차원뿐 아니라 캘리포니아주와 뉴욕주의 법제를 중심으로 구체적인 차별금지사유를 정리하였다. 데이터 수집 작업은 2025년 8월 30일을 기준으로 이루어졌다. 이 데이터셋은 국제적·비교법적 맥락에서 차별금지 규범을 분석하는 연구자들에게 기초 자료로 활용될 수 있다. Acknowledgement This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT)(No.RS-2025-02217259, Development of self-evolving AI bias detection-correction-explain platform based on international multidisciplinary governance, 50%) and in part by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2024S1A5B5A16029469, Data Feminism: Law and Policy, 50%) 이 성과물은 2025년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원(No.RS-2025-02217259, 뇌인지 다학제 국제 거버넌스 기반 인공지능 편향성 검출-교정-설명가능 지능적 자율진화 플랫폼 개발, 50%)과 2024년 대한민국 교육부와 한국연구재단의 지원(NRF-2024S1A5B5A16029469, 데이터 페미니즘 법정책에 관한 연구, 50%)을 받아 수행된 연구임
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it