Bibliographic record
Abstract
A great deal has been written lately about the ethics of globalization, understood as the intensification of interactions across national boundaries, particularly in the areas of trade and investment, but also the transfer of technology, the movement of peoples, and the global diffusion of a Western consumer lifestyle embodied in products such as Hollywood movies and McDonald's. There have been many impassioned ethical debates about the benefits and costs of these processes of globalization, including their effect on inequality both within and between societies, their consequences for the environment, and the way they are uprooting and displacing traditional ways of life. One striking aspect of these ethical debates about globalization is that they are themselves globalized. These debates take place across national boundaries, bringing together activists, academics, and government officials from all parts of the world, who must therefore find a common vocabulary to discuss their ethical concerns about globalization. People from Western liberal societies must find a way to discuss ethical issues with people from Buddhist societies in Southeast Asia or from indigenous communities in Latin America. Such transnational debates about ethics are increasingly unavoidable, given the intensity of interaction amongst the world's cultures. As globalization increases, ethics must itself become globalized. Our aim in this volume is to explore the globalization of ethics, which is a surprisingly neglected phenomenon. In particular, we examine how some of the world's most influential ethical traditions think about the task of constructing moral conversations and moral norms at a global level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.011 |
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".