Bibliographic record
Abstract
We put our hands in the mouth of the tiger, open the jaws, and count the teeth of the tiger. We are the Katkari. [Waghachya jabdyat, ghaluni haat, mojite daat, jaat aamchi, Katkaryanchi!] – A Katkari saying The origins of the gaothan problem are embedded in a complex historical process involving three main threads: Katkari migration from forested hills to the outskirts of caste villages on the coastal plain, integration into rural and migratory livelihoods where the Katkari could be easily bonded and systematic exclusion from the caste communities where Katkari hamlets are now located. The dual process of integration and exclusion (Kela 2006) sheds light on how and why the Katkari came to be so vulnerable to enclosure and eviction from their homes. This chapter traces the intermingling of these historical forces and concludes with a description of the Katkari's living conditions observed by the research team at the launch of the inquiry. Our historical reconstruction of the gaothan problem also illustrates the evolving relationship between the Katkari and the caste-based agrarian societies of the coastal districts of Maharashtra. We argue, as Heredia and Srivastava do in their 1994 study of the Katkari, that their vulnerability to external exploitation is not due to some inherent characteristics of their culture and communities. The Katkari, and for that matter other tribes of India (Béteille, 2008), do not have a fixed cultural identity linked to an unchanging past that can be labelled as primitive and backward.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".