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Ecological approaches to rural development projects

2001· article· en· W2146134740 on OpenAlexfundno aff
Sandra Dı́az, Daniel Cáceres

Bibliographic record

VenueCadernos de Saúde Pública · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsEcosystem servicesEnvironmental resource managementSustainabilityTotal human ecosystemContext (archaeology)Sustainable developmentPsychological resilienceEnvironmental planningEcosystemResilience (materials science)EcologyBusinessEcosystem healthGeographyEnvironmental scienceBiologyPsychology

Abstract

fetched live from OpenAlex

Most rural development projects include ecological considerations, and most conservation projects include some reference to sustainable development. However, conservation projects frequently fail because they do not incorporate local communities' perceptions and needs. Many development projects are also unsuccessful because they are not based on adequate ecological assessment. We focus here on the most important ecological issues to be addressed in order to place development projects in an ecosystem context. Such projects should incorporate updated and precise ecological concepts and methods. Some key ecological issues in development projects are the relationships between ecosystem functions, services, and sustainability, the concept of loose connectivity, the distinct and complementary concepts of ecosystem resistance and resilience, and the links between biodiversity and ecosystem functioning. We claim that an ecologically sound development project maximizes the preservation and improvement of ecosystem services, especially for local communities. We pose a series of questions aimed at placing rural development projects in an ecosystem context and suggest ways of organizing this information.

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)0.011
metaresearch head score (Gemma)0.014
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: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.010
Science and technology studies0.0070.027
Scholarly communication0.0100.008
Open science0.0030.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0130.001

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 head0.076
GPT teacher head0.228
Teacher spread0.152 · 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
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

Citations30
Published2001
Admission routes1
Has abstractyes

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