The Management of Common Property Resources: can Community-Based Organisations be a Viable Solution?. The Case Study of the Gram Sabbha in the Thanagazi Block, Rajasthan, India
Bibliographic record
Abstract
Can locally based collective action be a viable way to manage common property resources? Many scholars on collective action and common property are pessimistic * Post-doctoral researcher and lecturer, Universite de Montreal, Departement de Geographie, C.P., 6128, Succursale Centre-Ville Montreal (Quebec), H3C 3J7, Canada. This content downloaded from 207.46.13.117 on Sun, 23 Oct 2016 04:34:49 UTC All use subject to http://about.jstor.org/terms 38 Maria-Costanza Torri Revista Geografica 146 about the ability of people who face problems with common property resources to organize sustainable patterns of use for themselves. Some of them favour privatization of the commons as the only viable solution; others envisage the imposition of state regulation. In India, as in many developing countries, community organizations are playing an increasingly important role in the management of natural resources and biodiversity conservation, especially in the move towards decentralisation which is currently a priority of the government agenda. This article analyses the case study of the Thanagazi block (Rajasthan), India which represents an example of successful common resources conservation at community level achieved through a village organization, the Gram Sabbha. The results of the case study show that clear benefits may be derived from common property regimes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".