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Record W4402546124 · doi:10.1177/02780771241278086

Salish Perspectives on Human–Plant Relationships: Embracing Authenticity and Complexity

2024· article· en· W4402546124 on OpenAlexaff
Shandin Pete

Bibliographic record

VenueJournal of Ethnobiology · 2024
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNatural (archaeology)AppealPerspective (graphical)Environmental ethicsReciprocalSociologyEpistemologyNon-humanHistoryPolitical scienceLawPhilosophyComputer science

Abstract

fetched live from OpenAlex

This paper explores the intricate relationship between humans and plants from the perspective of Salish concepts, shedding light on the tradition of attributing human-desired qualities to the botanical world. Although plants possess traits that appeal to human desires, it is essential to recognize their inherent distinction from humans. Through historical utilization by Salish communities, a spiritual reciprocal bond has been established, necessitating the adherence to human-like protocols to maintain a symbiotic relationship. However, this exploration advocates against romanticizing this relationship, as it has the potential to foster internal stereotyping while leading to external discrepancies in philosophical pursuits. By carefully examining Salish practices from past to present, an emphasis is placed on the significance of comprehending and respecting the uniqueness of plant life. Through this analysis, the primary goal is to enhance our understanding of the profound connection between humans and plants while embracing the authenticity and complexity of this relationship. Appreciating the true nature of the bond can offer valuable insights into sustainable coexistence with the botanical world and contribute to fostering a more balanced and respectful relationship between humans and the natural environment.

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)0.002
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.050
Scholarly communication0.0060.006
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.238
GPT teacher head0.416
Teacher spread0.178 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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