Bibliographic record
Abstract
Abstract Global legal institutions such as UN agencies, the World Bank, and the World Trade Organization have become sites of some of the most innovative research in the social sciences, with implications that carry over into the methods and perspectives of legal scholarship. This chapter presents an outline of the history and methods of ethnography in global legal institutions as well as an account of the main findings of ethnographic work in this context. One of the key findings of ethnographic inquiry is that the structural failings and disenchantment of these institutions are at variance with persistent expressions of hope that are at the foundation of institutional ethics and self-representation. This hope is now being channelled into possibilities opened up by new information and communication technologies. These institutions are capitalizing on digitalization and innovations in technology as sources of decision-making. At the same time, new spaces have been created for private sector involvement in global governance initiatives, most prominently the UN Forum on Business and Human Rights. The UN, together with its corporate partners, is developing powerful information and communications technologies that present both important opportunities and risks in the administration of programmes in which artificial intelligence (AI) and machine learning are essential tools. These initiatives also present an important place for ethnographic research in presenting a clearer picture of the new geography of global governance and its legal frameworks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".