Bibliographic record
Abstract
Abstract Over the past twenty years, scholars in both anthropology and law (L) have found the approaches and concepts in Science and Technology Studies (STS) useful to understand techno-scientific transformations of the world. Legal scholars recognized that new scientific discoveries and technology interfered in the processes of routinization of social practices, creating new norms and influencing law. In the legal approach to STS, however, the focus has been on the law of the state and/or law deriving from the production of global governance institutions. Meanwhile, the encounter between anthropology and law has always had to take into consideration normatively effective mechanisms of social ordering that were not conventionally identified as law. Thus, the adoption of an STS perspective in legal anthropology was more open to exploring the normative power invested in other domains, such as the built environment, technologies, and inventories of knowledge and convictions such as religion. While L and STS are viewed as mutually constitutive of modernity, anthropological studies of legal pluralism (LP) have focused in recent years on multiple normative orders generated by world-making initiatives, including the normative power of technology under the influence of neoliberalism. In this contribution, then, we bring together law, science and technology studies, and legal pluralism to explore how normative orders are affected by materiality, technology, and scientific knowledge. In discussing the intersection of these three knowledge regimes, we find particularly useful concepts coming out of Actor Network Theory such as co-production, translation, boundary objects, and infrastructure.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".