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Record W1506989225

Asia Regional Workshop on Compensation for Ecosystem Services : a component of the global scoping study on compensation of ecosystem service

2007· preprint· en· W1506989225 on OpenAlexaboutno aff
K.V. Raju, S. Puttaswamaiah, Madhushree Sekher, R. Rumley

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystem servicesGeographyPovertyService (business)Developing countryPolitical scienceEconomic growthPaymentSocioeconomicsBusinessEcosystemEcologyMarketingSociologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

The World Agroforestry Centre, Nairobi, Kenya, together with Forest Trends, Washington DC, The World Conservation Union, Gland, Switzerland, Corporacion Grupo Randi Randi, Quito, Ecuador, the African Centre for Technology Studies, Nairobi, Kenya, the Institute for Economic and Social Research, Bangalore, India, and the United Nations Environment Programme – Division for Environmental Law and Conventions, Nairobi, Kenya, is leading a scoping study for the International Development Research Centre (IDRC-Canada) on the model of payments for environmental services (PES) as applied in developing countries, to determine how the poor are affected by these schemes and whether the schemes are compatible with poverty reduction objectives. As a key part of the study, a 3-day workshop is being held in each focal region. The Asia Regional Workshop was held in Bangalore, India from 8 -10 May 2006 at the Centre for Ecological Economics and Natural Resources (CEENR) of the Institute for Social and Economic Change (ISEC). The event brought together 39 participants from across the region, including India, Indonesia, Nepal and Sri Lanka, as well as the project coordination team from the Nairobi headquarters of The World Agroforestry Centre (ICRAF). Delegates represented international and national-level organizations, academic bodies, NGOs, consulting firms and donor agencies. This report covers the proceedings of the workshop. It includes summaries of all presentations made (22) as well as summaries of the panel discussions and the open discussions held after the presentations.

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 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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.068
GPT teacher head0.317
Teacher spread0.250 · 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 teacher head, 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

Citations3
Published2007
Admission routes1
Has abstractyes

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