Excluding the Poor from Their Rights: The Case of Natural Resources in West Bengal
Bibliographic record
Abstract
"I am presenting today on a three year seven village study looking at natural and social resource use by the poor in West Bengal. The study was conducted between 1993 and the end of 1996, and follows from work that I had done between 1986 and 1990 on a similar topic (Beck 1994; 1994a). This is the first time to our knowledge that use of natural resources by the poor has been covered systematically in a study in either post-independence West Bengal, or Bangladesh (although I believe there has been some research on this topic recently by Syed Hashemi in Bangladesh). This is itself an interesting point, because there have been several other studies of CPR use elsewhere in South Asia, so as well as all the other biases against recognition of the importance of CPRs there is also in this case a geographical bias to which I will return. For further details of the study see Ghosh (1988)."
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".