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Record W2329281342 · doi:10.5751/es-06896-190426

Culture, Nature, and the Valuation of Ecosystem Services in Northern Namibia

2014· article· en· W2329281342 on OpenAlexvenueno aff
Michael Schnegg, Robin Rieprich, Michael Pröpper

Bibliographic record

VenueEcology and Society · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
FundersBundesministerium für Bildung und Forschung
KeywordsEcosystem servicesValuation (finance)Environmental resource managementEcosystemBusinessEcosystem valuationNatural resource economicsGeographyEnvironmental planningEcologyEconomicsEcosystem healthBiologyFinance

Abstract

fetched live from OpenAlex

Defining culture as shared knowledge, values, and practices, we introduce an anthropological concept of culture to the ecosystem-service debate. In doing so, we shift the focus from an analysis of culture as a residual category including recreational and aesthetic experiences to an analysis of processes that underlie the valuation of nature in general. The empirical analysis draws on ethnographic fieldwork conducted along the Okavango River in northern Namibia to demonstrate which landscape units local populations value for which service(s). Results show that subjects perceive many places as providing multiple services and that most of their valuations of ecosystem services are culturally shared. We attribute this finding to common experiences and modes of activities within the cultural groups, and to the public nature of the valuation process.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.199
Teacher spread0.195 · 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

Citations39
Published2014
Admission routes1
Has abstractyes

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