MétaCan
Menu
Back to cohort
Record W2924809373

INDIGENOUS PEOPLES’ RIGHTS OVER FOREST RESOURCE GOVERNANCE IN INDIA AND CANADA: DEBATING THE ROLE OF DECENTRALIZATION

2018· dissertation· en· W2924809373 on OpenAlexaboutno aff
Sheethal Padathu Veettil

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationIndigenousPolitical scienceCorporate governanceIndigenous rightsResource (disambiguation)Public administrationGeographyEconomic growthDevelopment economicsHuman rightsBusinessEcologyLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

India and Canada have a common colonial past which deeply disturbed the close ecological relationship between Indigenous peoples and forests, with the colonial authorities restricting Indigenous peoples’ legitimate use of forest resources. Even during the post-colonial period, the forest policies in both these countries continued centralized conservation mechanisms that excluded Indigenous peoples. However, the past few decades have witnessed a shift towards decentralized governance in both jurisdictions. This recent trend manifests an attempt to devolve powers and authority to Indigenous institutions. In India, the constitutional recognition of decentralized governance and the enactment of the Scheduled Tribes and Other Traditional Forest Dwellers (Recognition of Forest Rights) Act, 2006 emphasized the significance of traditional tribal institutions in resource governance. By contrast, in Canada, although the development of co-management regimes is sometimes highlighted as showing the changing face of forest resource governance, the devolution of power to traditional Aboriginal institutions in decision-making over resources remains unsettled. The first chapter sets a background for the thesis by giving an introduction to the history of alienation faced by the indigenous peoples in India and Canada during colonial times. It further expands on the rationale for choosing both the jurisdiction as two comparable units. The second chapter provides a theoretical framework to the thesis by discussing various theories on decentralization. This part highlights the role of indigenous peoples and their institutions in forest resource governance. The third chapter examines the efforts towards decentralization in India through the Constitutional recognition and enactment of the FRA. Here it is argued that a radical shift in the tribal self-governance in India has achieved decentralization of forest resource governance. This argument is developed through an analysis of the implementation of the FRA at Mendha Lekha in Maharashtra. Some of the important judicial decisions that legitimized the decision-making powers of the tribal institutions in the forest resource governance are also discussed at this juncture. The fourth chapter analyzes decentralized forest governance in Canada through the evolution of co-management. Through an illustration of Clayoquot Sound in British Columbia, it is argued that there is an ongoing absence of strong decentralized institutional arrangements for decision-making on forest resource governance in Canada. Some of the important judgments of the Supreme Court of Canada on duty to consult and accommodation are also discussed here to argue that an absence of a Constitutional recognition of these rights as compared to India has limited the scope of judicial interventions that legitimizes Aboriginal consent in the resource governance. The fifth chapter offers concluding remarks by comparing both the jurisdictions. Through a comparative analysis, this part argues that the FRA provides a stronger platform for the decentralization of forest governance in India than the co-management efforts in Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.148
Threshold uncertainty score0.989

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0340.022
Scholarly communication0.0110.003
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.002
GPT teacher head0.166
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicConservation, Biodiversity, and Resource ManagementFrench-language works237,207