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Record W4284991398 · doi:10.1093/biosci/biac053

Reciprocal Contributions between People and Nature: A Conceptual Intervention

2022· article· en· W4284991398 on OpenAlexafffund
Jaime Ojeda, Anne K. Salomon, James K. Rowe, Natalie C. Ban

Bibliographic record

VenueBioScience · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Cultural Studies in Latin America and Beyond
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReciprocity (cultural anthropology)ReciprocalIndigenousRestructuringSustainabilityRelevance (law)Intervention (counseling)SociologyEnvironmental ethicsEpistemologyEcologyPolitical scienceSocial sciencePsychologyBiologyLaw

Abstract

fetched live from OpenAlex

Abstract Throughout human history, Indigenous and local communities have stewarded nature. In the present article, we revisit the ancestral principle of reciprocity between people and nature and consider it as a conceptual intervention to the current notion of ecosystem services commonly used to inform sustainability transformation. We propose the concept of reciprocal contributions to encompass actions, interactions, and experiences between people and other components of nature that result in positive contributions and feedback loops that accrue to both, directly or indirectly, across different dimensions and levels. We identify reciprocal contributions and showcase examples that denote the importance of reciprocity for our ecological legacy and its relevance for biocultural continuity. We suggest that the concept of reciprocal contribution can support transformation pathways by resituating people as active components of nature and restructuring institutions so that ethical principles and practices from Indigenous and local communities can redirect policy approaches and interventions worldwide.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.077
Scholarly communication0.0090.011
Open science0.0020.016
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.006
GPT teacher head0.239
Teacher spread0.233 · 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 designTheoretical or conceptual
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

Citations98
Published2022
Admission routes2
Has abstractyes

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