JOINT FOREST MANAGEMENT IN INDIA: ASSESSMENT OF PERFORMANCE AND EVALUATION OF IMPACTS
Bibliographic record
Abstract
This paper is an attempt to critically survey the movement for Joint Forest Management(JFM) in India. The study commences with a description of the policy context in which Joint Forest Management got initiated in India. It considers the 1980s to have effected a paradigm shift in India's forest policy and legislations. The passage of the Forest Conservation Act in 1980 was followed by a host of measures to unleash a forest conservation movement in India based on local community support. The National Forest Policy of 1988 marked the first effort to set the pace for community participation in forest management. In June 1990, the Government of India issued a circular to give effect to the provisions of the National Forest Policy 1988 in this regard. Joint Forest Management was thus born in India. By the year 2002, JFM covered 140,953 sq kilometers of forest area, which was distributed amongst 63,618 Forest Protection Committees (FPCs) in different States. This was a major achievement, considering the fact that forest management in India has, since 1878, been a bureaucratically driven process. The first chapter also describes the main features of JFM. However, as the paper proceeds to state in Chapter 2, the spread of JFM in India has been uneven in scope and structure. In most of the States, JFM has not even covered degraded forests in their entirety. Nomenclatures and the governing structures of JFM vary from State to State. Indeed, in most States, Forest Protection Committees do not enjoy any legal status and in very many cases they are liable to be disbanded by the Forest Department. Similarly very few FPCs are given primary powers both in terms of rights to frame rules and management plans and exercise of executive and legal functions. These powers continue to be vested with the forest department in a large number of cases. The chapter also assesses the performance of different States in regard to JFM. It adopts the criteria of spatial spread, delegation of powers, and "empowerment" for evaluating the performance of JFM in different States. Where available, field level information regarding the actual functioning of JFM is also adduced to support findings. The chapter notes JFM to be successful in the States of Andhra Pradesh, Haryana, Madhya Pradesh and West Bengal. It further notes that the new States of Jharkhand, Chattisgharh and Uttaranchal have exhibited a keen commitment to JFM, reckoned in terms of forest areas brought under the system in these States. Chapter 3 of the paper provides a diagnosis for the poor performance of JFM. The chapter states that the poor performance of JFM in different areas has also been on account of insufficient "empowerment" and undesirable regulations. Moreover, in many cases JFM has been spurred through externally aided projects. Chapter 4 surveys the principal policy changes that have been brought about more recently (2000-2003), in response to observed problems. The chapter notes the significant strides made by the Government of India to strengthen JFM policies in different States through these policy changes. Providing legal status to Forest Protection Committees, establishing a monitoring cell at the Central Government level, extending JFM to good forest areas and establishing forest development agencies to directly channel funds to the FPCs, have formed the features of policy change in recent times. Chapter 5 advances the methodology of 'PEER' for assessing the functioning and impacts of JFM in India. The chapter states that in the case of JFM, the 'P' element of PEER, which stands for 'philosophy' and 'policy-intent', provides the foundation for the 'EER' ('efficiency' 'effectiveness' and 'ramification') elements. Unfortunately, existing assessments of the JFM implementation process have-by confining their attention to the 'EER' elements-lost sight of the 'policy intent' element. The chapter discusses the significance of approaching JFM from a 'philosophical' or 'policy intent' angle. Conflicts between the objectives of the national and State governments as well as sub-national policy intents can contribute much to understanding the causes of observed inter-State differences in performance. The chapter also mentions the evolving debate regarding corporatization of forest management. It notes that in the context of the economic liberalization process in India, the key philosophical issue before policy is the reconciliation of communitarian ideals with privatization programs underlying the process of economic reforms. The chapter marshals field-level evidence to draw inferences regarding the 'efficiency', 'effectiveness' and 'ramifications' dimensions of JFM in India. Finally, the paper concludes by summarizing the discussions regarding the policy trajectory and politics of JFM in India.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".