The political economy of human rights organizations’ codes of ethics
Bibliographic record
Abstract
Purpose This paper scrutinizes the impact of socioeconomic, political, legal and religious factors on the internal ethical values of human rights organizations (HROs) worldwide. The authors aim to examine the Code of Ethics for 279 HROs in 67 countries and the social and legal settings in which they operate. Design/methodology/approach Using the framework of protect, respect and remedy, the authors look for keywords that represent the human rights lexicon in these three areas. In the protection of human rights, the authors select the terms: peace, transparency, freedom and security. For the respect of humans, the authors use the terms: dignity, equality, respect and rights. Sources of remedies come from justice and ethics. The analysis seeks to determine what political economy settings drive the ethical value choices of the organizations. Those choices are proxied by those keywords they mention in their Code of Ethics. Findings The analysis show that the scope of ethical values mentioned are higher when the HRO is in a country with more domestic violence, lower income inequality, French civil or Islamic legal origin and higher trust in politicians. In regard to the determinants of the ten keywords individually, the authors conclude that the status of the socioeconomic, political, religious and legal settings impact with local HROs mention each of the keywords: peace, justice, transparency, dignity, equality, ethics, respect, freedom, security and rights. Research limitations/implications The analysis is based on HROs that have a webpage in English and list the employee Code of Conduct. Originality/value This study is the first to examine the Code of Ethics for HROs. The authors demonstrate that country-specific characteristics help to drive their internal ethical values.
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How this classification was reachedexpand
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".