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Record W2600940533 · doi:10.1080/13504509.2017.1304462

Non-timber forest products and village livelihoods in Rajasthan, India: adaptation in a changing environment

2017· article· en· W2600940533 on OpenAlexafffund
David Natcher, Vijayalakshmi Kalagnanam, Ramesh Rawal, Mark Johnston, Abdullah‐Al Mamun

Bibliographic record

VenueInternational Journal of Sustainable Development & World Ecology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of SaskatchewanSaskatchewan Research Council (Canada)Global Institute for Water Security
FundersCanadian International Development Agency
KeywordsLivelihoodForest coverBusinessGeographySocioeconomicsAgroforestryEnvironmental planningAgricultureNatural resource economicsEnvironmental protectionEconomicsEcology

Abstract

fetched live from OpenAlex

This paper presents the results of research conducted between 2009 and 2014 in the village of Khanda Sharol in the state of Rajasthan, India. Our research objective was to determine how the livelihoods of village residents have been affected by the intensification of forest use, and the resulting loss of domestic access to traditionally used forest resources. Results indicate that changes in forest cover have resulted in a loss of livelihood options for village residents. Yet rather than being victimized by environmental change processes, this paper shows how villagers have responded by partnering with public and private actors to develop a community protected forest area that is now helping villagers to meet their livelihood needs. These findings suggest that sustainable livelihoods in rural regions of India require committed and scaled approaches involving local, public, and private actors.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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 teacher head, not a consensus.

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

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

Citations9
Published2017
Admission routes2
Has abstractyes

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