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Record W2975417546 · doi:10.23846/b/sr/201906

Payment for environmental services

2019· report· en· W2975417546 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsnot available
FundersFP7 International CooperationDeutsche Gesellschaft für Internationale Zusammenarbeit
KeywordsFragilitySocioeconomic statusQuarter (Canadian coin)GermanChristian ministryDistressPaymentBusinessEconomic growthDevelopment economicsDemographic economicsPolitical scienceEconomicsGeographyPsychologyDemographySociologyFinance

Abstract

fetched live from OpenAlex

Nearly a quarter of people in the world live in fragile contexts.In addition to being at greater risk of violence and psychological distress, they lag behind global averages on a range of socioeconomic outcomes.As conflict and fragility continue to rise, many development agencies, including the German Federal Ministry for Economic Cooperation and Development (BMZ), have identified supporting fragile states as a key priority.In 2016,

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.531
Threshold uncertainty score0.670

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5310.167

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 head0.042
GPT teacher head0.325
Teacher spread0.283 · 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.

Study designNot applicable
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

Citations5
Published2019
Admission routes1
Has abstractyes

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