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Record W2967510548 · doi:10.14237/ebl.10.1.2019.1363

Understanding Canoe Making as a Process of Preserving Cultural Heritage

2019· article· en· W2967510548 on OpenAlexaff
Débora Peterson, Natália Hanazaki, Fabiana Li

Bibliographic record

VenueEthnobiology Letters · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsIndigenousFishingIdentity (music)EthnologyGeographyCultural heritageCultural identityAnthropologyHistoryArchaeologySociologyEcologySocial scienceAestheticsArt

Abstract

fetched live from OpenAlex

Canoes are deeply ingrained elements of the Caiçara culture, not only for their historical and current practical uses, but also for their socio-cultural outcomes. Caiçara people are the descendants of Europeans, Africans, and Indigenous peoples who inhabit parts of the Atlantic Forest in the southern and southeastern coast of Brazil. Despite this, canoe making has been declining in several Caiçara communities, while many ongoing initiatives have attempted to encourage the maintenance of this practice. This article explores some of the Caiçara-canoe relationships within the Juatinga Ecological Reserve, in southeastern Brazil. We discuss how canoes are an appropriate technology for some fishing techniques, and are thus not easily replaced by fiberglass or aluminum boats. We also explore some socio-cultural dimensions of canoe making in light of the relationships of Caiçara canoe makers and fishers with the forest and with other community members. This article contributes to a growing body of knowledge to protect elements of Caiçara identity, including initiatives to help maintain canoes, canoe making, and the people involved with them.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.026
Scholarly communication0.0080.006
Open science0.0010.005
Research integrity0.0020.002
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 head0.106
GPT teacher head0.289
Teacher spread0.183 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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