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Record W4212977316 · doi:10.3389/fcosc.2022.791103

Human Dimensions of the Reintroduction of Brazilian Birds

2022· article· en· W4212977316 on OpenAlexaff
Flávia de Campos Martins, Mônica T. Engel, Francine Schulz, Cláudia Sofia Guerreiro Martins

Bibliographic record

VenueFrontiers in Conservation Science · 2022
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWildlifeEndangered speciesNoveltyWildlife conservationPsychological interventionWildlife managementEnvironmental planningEnvironmental resource managementCitizen journalismAction (physics)GeographyBusinessEcologyPolitical sciencePsychologyBiologyHabitat

Abstract

fetched live from OpenAlex

People's acceptability for wildlife, stakeholders' engagement and involvement are acknowledged as key factors for the success of wildlife reintroduction projects. We analyzed the main National Action Plans (NAPs) (the Brazilian management participatory instrument for the conservation of endangered species) for eight bird species and conducted an online questionnaire with researchers and practitioners involved in those species reintroduction programs. The assessment of the main Brazilian bird's reintroduction programs showed that, in general, efforts have been made to integrate local people into it. Nevertheless, the actions were disconnected, isolated and fragmented. A formal protocol, designed, discussed and approved by experts aiming to address the human dimensions (HD) of human-bird interactions (HBI), preferably to be used in each stage of the reintroduction programs, was not found. Actions considered related to human dimensions are mainly under the umbrella of environmental education interventions or campaigns, more directed to children and youth; correspond to activities performed by locals with the birds and/or captive birds facilities; or, fostering artcraft production or bird watching activities. The weak or sometimes absent human dimensions approach to this important conservation tool may indicate either the novelty for Brazilian researchers and managers of the science of human dimensions within the field of wildlife management or the lack of dialogue between natural and social sciences when wildlife conservation is at stake. Reintroductions are expensive, sensitive, and labor-intensive processes. It becomes necessary due the conservation status of the species and its implementation follows a careful research of biological, ecological and socio-institutional regional background that identifies the drivers of species extinction and plans according to it. Understanding and predicting people's behaviors and its triggers are paramount to successful reintroduction projects. Thus, making use of well-planned HD studies in HBI may be the watershed between success or failure of reintroduction programs. This study was a pioneer initiative of its kind and it aimed to provide sound recommendations for managers, researchers and practitioners to acknowledge the relevance of HD and its core role in the reintroduction of endangered bird species.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.033
GPT teacher head0.317
Teacher spread0.284 · 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
Published2022
Admission routes1
Has abstractyes

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