Bibliographic record
Abstract
As calls for reparations to indigenous peoples grow on every continent, issues around resource extraction and dispossession raise complex legal questions. What do these disputes mean to those affected? How do the narratives of indigenous people, legal professionals, and the media intersect? In this richly layered and nuanced account, Pooja Parmar focuses on indigeneity in the widely publicized controversy over a Coca-Cola bottling facility in Kerala, India. Juxtaposing popular, legal, and Adivasi narratives, Parmar examines how meanings are gained and lost through translation of complex claims into the languages of social movements and formal legal systems. Included are perspectives of the diverse range of actors involved, based on interviews with members of Adivasi communities, social activists, bureaucrats, politicians, lawyers, and judges. Presented in clear, accessible prose, Parmar's account of translation enriches debates in the fields of legal pluralism, indigeneity, and development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".