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Record W3037500426 · doi:10.4103/cs.cs_18_123

Do We All Speak the Same Language When Talking Conservation? Caiçara Understandings of Conservation in their Landscape

2020· article· en· W3037500426 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.

Bibliographic record

VenueConservation and Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIndigenousSeascapeGovernment (linguistics)General partnershipSociologyCommunity-based conservationEnvironmental resource managementPublic relationsPolitical scienceEnvironmental planningGeographyEcology

Abstract

fetched live from OpenAlex

Based on their world view, indigenous and local communities may have their own concepts of conservation, which may be different from Western ideas of conservation. Here we report the results of a photovoice study with a Caiçara community in the Juatinga Ecological Reserve, a protected area in the Brazilian Atlantic Forest region. Participants were asked to take photos of their landscape/seascape to illustrate what they understand as conservation. Photos produced by the participants served as 'boundary objects' that helped to evoke feelings, ideas, and thoughts of people-nature relationships during individual interviews, and finally during a group discussion. The results helped to explore ways to frame a Caiçara concept of conservation and highlight the importance of developing place-based conservation projects and approaches meaningful for Caiçara people. Such initiatives can help in understanding Caiçara motivations for conservation, aid partnership-building, and promote knowledge co-production between community, government managers and other stakeholders.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.147
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.456
GPT teacher head0.513
Teacher spread0.058 · 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