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Record W1857753816 · doi:10.3368/le.89.3.432

Ecopayments and Deforestation in Costa Rica: A Nationwide Analysis of PSA’s Initial Years

2013· article· en· W1857753816 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLand Economics · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaEarth Institute, Columbia UniversityTinker FoundationStyrelsen för Internationellt Utvecklingssamarbete
KeywordsDeforestation (computer science)ClearingPaymentHectareGeographyEconomicsAgricultural economicsNatural resource economicsAgricultureArchaeologyFinance

Abstract

fetched live from OpenAlex

We offer a nationwide analysis of the initial years of Costa Rica’s PSA program, which pioneered environmental-services payments and inspired similar initiatives. Our estimates of this program’s impact on deforestation, between 1997 and 2000, range from zero to one-fifth of 1% per year (i.e., deforestation is avoided on, at most, 2 out of every 1,000 enrolled hectares). The main explanation for such a low impact is an already low national deforestation rate. We also consider the effect of enrollment. Predicted deforestation on enrolled versus nonenrolled hectares, and matching analyses suggest an enrollment bias toward lower clearing threat. Enrolling land facing higher threat could raise payments’ impact on deforestation. <i>(JEL Q24, Q28)</i>

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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