On the Matter of Resources and Techno‐Politics: The Case of Water and Iron in the South Indian Iron Age
Bibliographic record
Abstract
ABSTRACT Water management and iron production were two socio‐technical practices deeply entrained with the politics of emerging social distinctions in northern Karnataka during the South Indian Iron Age (1200–300 BCE). In this article, we approach resources by building a theoretical convergence between “resource materialities” and “techno‐politics,” which allows us to assess the historically specific constitution of certain materials as culturally valued resources while maintaining analytical attention on how assemblages of technical practices and active material properties shape social conditions. By differentially anticipating and responding to the social and material distributions of a range of dynamic matter (for example, granitic rock, iron ores, bloom, and metal, water, soils, and vegetation), Iron Age peoples transformed substances into resources and simultaneously produced a historically unique political sociology of resource relations. Our approach dissolves the processual distinction between natural resource and cultural product and directs attention to how substances become resources through ongoing historical articulations of humans and nonhumans in contexts oriented by cultural values. Contrasting the material properties and distributions of iron and water resource assemblages allows us to more fully understand the distinctiveness of different forms of techno‐politics and resource relations within the same cultural and historical context. [resource materialities, techno‐politics, resource assemblage, entrainment, South Indian Iron Age]
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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 | 0.001 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".