Path Dependence, Multiple Equilibria, and Adaptive Efficiency in Forest Regimes in India
Bibliographic record
Abstract
"The evolution of forest regimes in India has almost completed a full cycle from the community regime in the pre-British period through state regimes during the British colonial period and the first post-independence phase and finally back to community based regimes in the 1990s. During the British period that evolution may be characterized as discontinuous, in the standard (temporal) sense, but path-dependent in a geographical sense. Change resulted from the imposition of a new organizational structure with enough energy to dismantle the existing structure; its geographical path-dependency reflected the inertia of the British organizational structure developed in other countries. Regimes changes in the post-British period have been path-dependent (in the temporal sense) due to self-reinforcing mechanisms,among which organizational inertia has been the dominant one. fa the post 1987 phase,external factors(outside the government and the forest department), such as non-governmental organizations and peoples initiatives at the local level have moved the process closer to one of adaptive efficiency. However, multiple forest regimes have been present at all times. An argument is made for development of a theory of evolution of resource regimes that incorporates interactions between formal institutions and the informal institutions of user groups of the state's forestry administration."
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".