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Record W1828954313 · doi:10.1002/jid.1820

POVERTY REDUCTION THROUGH PROMOTING ALTERNATIVE LIVELIHOODS: IMPLICATIONS FOR MARGINAL DRYLANDS

2011· article· en· W1828954313 on OpenAlexaff
Bhim Adhikari

Bibliographic record

VenueJournal of International Development · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsLivelihoodLand degradationSustainabilityNatural resource economicsPovertyPoverty reductionBusinessSustainable developmentEnvironmental planningEconomicsEconomic growthGeographyAgriculturePolitical science

Abstract

fetched live from OpenAlex

Abstract Land degradation has become a most pressing issue in drylands. Although crop and livestock production have been the dominant sources of income, there is an increasing realisation that alternative livelihood options need to be explored to ensure both environmental and economic sustainability. This paper provides a systematic review of alternative livelihood strategies currently adopted for improving livelihood conditions of dryland dwellers in different parts of the world. Although drylands encompass a vast social and geographic terrain and represent a heterogeneous socio‐economic environment, this paper has been able to document some general lessons applicable to rural drylands for diversifying livelihood opportunities and promoting rural development while minimising the pressure from intensive land‐based activities. The paper concludes with a discussion on how international development policies can move forward in the fight against land degradation and in helping achieve poverty reduction through investments in sustainable land management and alternative livelihood strategies. Copyright © 2011 John Wiley & Sons, Ltd.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.090
GPT teacher head0.293
Teacher spread0.202 · 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 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

Citations24
Published2011
Admission routes1
Has abstractyes

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