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Record W2261712385

Learning the Livelihoods Way: Understanding Rural Livelihood Sustainability in Northeastern Cambodia

2003· article· en· W2261712385 on OpenAlexfundno aff
Rebecca Kinakin

Bibliographic record

VenueYork University Digital Library (York University) · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicCambodian History and Society
Canadian institutionsnot available
FundersMinistry of Education, IndiaMinistry of Agriculture, Forestry and FisheriesInternational Development Research Centre
KeywordsLivelihoodSustainabilityGeographyRural developmentEconomic growthEnvironmental planningEnvironmental resource managementBusinessEconomicsAgriculture
DOInot available

Abstract

fetched live from OpenAlex

In Cambodia, as in rural areas worldwide, serious problems of increasing poverty, declining traditional livelihood activities, decreasing access to resources, and growing ecological destruction are necessitating new approaches to socio-economic development and conservation. Community Based Natural Resource Management (CBNRM) stands to play an important role in enhancing rural livelihood sustainability; however such programs and strategies are presently facing many challenges. The Sustainable Livelihoods (SL) approach has been linked to the better overall planning and implementation of activities, projects, and programs. Proponents of SL claim that it can facilitate a deeper understanding of rural dynamics and realities and, in turn, yield a wider reach for reducing poverty and enhancing social and ecological well-being. 
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\nThis paper explores: a) the use of a livelihoods approach as a way to increase understanding of rural livelihoods, and the factors that affect them, in less-known Mondulkiri province, northeastern Cambodia, and b) the implications of a livelihoods approach for CBNRM in Cambodia. It was found that the linking of CBNRM and SL has the potential to improve CBNRM practices, the deeper awareness and understanding brought by a livelihoods approach helping to ensure that CBNRM fits appropriately into the lives of people, as opposed to fitting people into CBNRM. And, although not a magic bullet, it does seem possible using a livelihoods approach to gain considerable insight into how to build sustainable rural livelihoods based on local people's meanings, experiences, and desires of livelihood, well-being, and sustainability.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.538
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.186
Teacher spread0.171 · 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.

Study designQualitative
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

Citations0
Published2003
Admission routes1
Has abstractyes

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