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Social Participation in Management Councils of Protected Areas: Normative advances and the perspective of ICMBio Environmental officers

2020· article· en· W3094206521 on OpenAlexaff
Deborah Santos Prado, Luciana Gomes de Araujo, Paula Chamy, Ana Carolina Esteves Dias, Cristiana Simão Seixas

Bibliographic record

VenueAmbiente & sociedade · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDevelopment, Ethics, and Society
Canadian institutionsUniversity of Waterloo
FundersUniversidade Estadual de CampinasInstituto Chico Mendes de Conservação da BiodiversidadeCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsNegotiationNormativePerspective (graphical)Political scienceParticipatory managementDemocracyPerceptionPower (physics)Public relationsSociologyEnvironmental planningPsychologyPoliticsSocial psychologyGeographyLaw

Abstract

fetched live from OpenAlex

Abstract Management councils of Protected Areas are an important tool to the exercise of social participation of individuals and groups struggling for social-environmental causes in Brazil’s democracy. This paper aims to integrate the main regulations guiding the social participation in Management Councils of Protected Areas in Brazil and the perception of managers and technicians in order to understand the process of elaboration of the rules, the behind the scenes, and negotiations. Our findings highlight that social participation has been formally ensured in many aspects, revealing democratic advancements in the field of Protected Areas management in Brazil. However, despite remarkable progress, many challenges remain, including aspects of representation, independency, level of influence, and sharing power in decision-making processes. The outcomes of participation are ongoing processes of learning and negotiation, which are reflected in the improvement of the legal arrangements analyzed.

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)0.014
metaresearch head score (Gemma)0.016
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 score0.035
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0140.028
Scholarly communication0.0080.004
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.314
Teacher spread0.285 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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