Bibliographic record
Abstract
has quickly established himself as an anthropologist who is widely read in theory and in international development studies and has particular skills in utilizing research questions and ideas from the burgeoning field of science and technology studies.He uses these diverse tools to analyze questions that have been traditionally addressed with the tools of international political economy: deforestation, peasant and Indigenous land dispossession, export-oriented monocrops, genetically modified organisms, and global agribusiness.These issues are now also addressed by newer work on "the anthropology of the state," which overlaps a great deal with much law-and-society research on both nationstate and transnational issues.1 Although Hetherington defines himself primarily as an anthropologist of what he and others call the Anthropocene (we shall reflect on that self-identity below), it may be helpful for a law-and-society readership to begin not with the obvious cross-over between his research and environmental justice 2 but, rather, with his significant contribution to the anthropology of the state.And, as in his previous book, documenting how a state (Paraguay) that is optimistic about its democratic future despite its authoritarian past encourages ordinary people to act as "guerrilla auditors" (Hetherington 2011), the book under review focuses on a short-lived experiment in regulation undertaken with progressive intentions.The well-meaning, but often fruitless, efforts of progressives who came from the world of human rights non-governmental organizations (NGOs) into the state under Mariana Valverde is professor emeritus at the Centre for Criminology and Sociolegal Studies, University of Toronto.Her most recent book is Infrastructure: New Trajectories in Law (Routledge, 2022).m.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".