Bibliographic record
Abstract
The small victory for the 47 families with solid title to their land in Nadodewadi rekindled the inquiry's sense of purpose and inspired the Katkari to take up once again the goal of acquiring gaothan for their hamlets. Over a period of several months, the various gaothan action committees once again began to contact the research team and ask for meetings. The threat of eviction was as tangible as ever. People said that they wanted to do something, but were not sure what. The research team was not sure either. We agreed, however, to explore the many issues that people were raising about why people in some hamlets were not willing to take a public stand on the gaothan issue, and to use this understanding to plan future actions. Fear of the landholders was very much at the forefront during these discussions and became a key question the Katkari wanted to explore further. This chapter discusses crucial results from the efforts to make sense of the Katkari's fear of landholders immediately following the gram sabha process. While for many Katkari, overcoming their fear initially seemed like an impossible task, their own interpretations of the threats they faced launched several new lines of action and inquiry. These unfolded in unexpected ways and at different rates. We needed new participatory inquiry and planning methods to take us beyond the simple chain of cause and effect revealed through the problem tree.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".