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The mixed experience of private sector involvement in biodiversity management in Costa Rica

2002· book-chapter· en· W967158565 on OpenAlexaboutno aff
Michael Sturm

Bibliographic record

VenueCambridge University Press eBooks · 2002
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsTourismLatin AmericansSloganQuarter (Canadian coin)GeographyPrivate sectorEconomic growthPolitical scienceParadiseEnvironmental educationEcotourismBusinessEnvironmental protectionSocioeconomicsSociologyEconomicsHistory

Abstract

fetched live from OpenAlex

Biodiversity under threat in Costa Rica The small Latin American country of Costa Rica is generally known as ‘environmentally friendly’, and therefore has become a paradise for thousands of ecotourists. The Costa Rican Tourism Institute (ICT, Instituto Costarrecense de Turismo) has initiated an expensive advertising and image promoting campaign with the slogan ‘Costa Rica – no artificial ingredients’. The target groups are North American citizens (USA and Canada), between 25 and 54 years of age, who earn $75,000 or more a year, and have a university education ( Tico Times , 7 Aug. 1998). In 1999, the number of tourists reached 1 million ( Tico Times , 17 Dec. 1999). A quarter of the country is considered to be protected (see Fig. 11.1). The World Bank and the Global Environmental Facility (GEF) have spent millions of dollars to support official nature conservation measures and the responsible governmental departments. NGOs provide information and environmental education on site. Nevertheless, the current condition of the biodiversity in Costa Rica is disappointing. Despite regulations, management initiatives and international financial support, Costa Rica, formerly densely forested, has become an agricultural country. Virgin forests have become rare and are found nowadays only in remote or protected areas. Since the arrival of multinational companies, large areas have been transformed into monocultures, resulting in the pollution of both soil and water.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.025
GPT teacher head0.176
Teacher spread0.151 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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