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Record W4402918239 · doi:10.1515/bis-2023-0031

Green Basic Income: Evaluating the Bolsa Verde Project in the Brazilian Amazon

2024· article· en· W4402918239 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBasic Income Studies · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsOntario Tech University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCape verdePovertyBasic incomeAmazon rainforestContext (archaeology)Deforestation (computer science)Political scienceEconomic growthBusinessEconomicsGeographySociology

Abstract

fetched live from OpenAlex

Abstract We analyze the Bolsa Verde Program, arguing that it likely was the world’s first largescale institution of a Green Basic Income Program. As such, the initiative presents a unique opportunity to evaluate the potential environmental uses and implications of Basic Income initiatives. Our study relies on a socially-embedded analysis of the program as it functioned in the context of the Brazilian Amazon. This involves analysis of qualitative data from former program beneficiaries, community leaders, program evaluators, and managers. This research suggests that the program operated socially as a de facto Green Basic Income program, despite being designed as a hybrid Payment for Environmental Services initiative. Our analysis suggests that Bolsa Verde was successful in reducing both deforestation and poverty, and these successes were achieved without undermining collective community institutions that could have positive anti-poverty and environmental protection benefits of their own.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.054
GPT teacher head0.325
Teacher spread0.271 · 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