In the Shadow of the Anticommons: The Paradox of Overlapping Exclusion Rights and Open-Access Resource Degradation in India's Wastelands
Bibliographic record
Abstract
India's wastelands have been classified as both over utilized and underutilized. Perplexingly, an apparent tragedy of the commons exists alongside extensive official management powers. In this paper, I argue that the complexity of governance structures may be inadvertently worsening the situation. Looking to recent work on contested property and the anticommons concept, I suggest that an anticommons amongst those officially controlling the lands is casting a long and unexpected shadow by encouraging the emergence of open-access de facto resource exploitation and discouraging de facto management. This extension of the anticommons concept implies that the effects of anticommons are not necessarily limited to under exploitation, as most commonly used in the developing anticommons literature. It points to a wider examination of the harmful effects of anticommons situations.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.056 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".