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Record W3112549476 · doi:10.14237/ebl.11.2.2020.1640

Learning about Extraordinary Beings: Native Stories and Real Birds

2020· article· en· W3112549476 on OpenAlexaboutno aff
Raymond Pierotti

Bibliographic record

VenueEthnobiology Letters · 2020
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousTricksterSociocultural evolutionShamanismEcologyAnthropologySociologyEnvironmental ethicsEthnologyHistoryGeographyBiologyArchaeology

Abstract

fetched live from OpenAlex

Oral traditions of Indigenous American peoples (as well as those of other Indigenous peoples) have long been discussed with regard to their reliability as metaphorical accounts based upon historical knowledge. I explore this debate using stories to discuss the importance of the role of Corvidae in Indigenous knowledge traditions and how these stories convey information about important socioecological relationships. Contemporary science reveals that Corvids important in cultural traditions were companions to humans and important components of the ecology of the places where these peoples lived. Ravens, Crows, Jays, and Magpies are identified as having special roles as cooperators, agents of change, trickster figures, and important teachers. Canada (or Gray) Jays serve as trickster/Creator of the Woodland Cree people, Wisakyjak. Magpies won the Great Race around the Black Hills to determine whether humans would eat bison or vice versa. I analyze these stories in terms of their ecological meaning, in an effort to illustrate how the stories employ dramatic settings to encourage respect and fix relationships in the sociocultural memory of the people.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.317
Teacher spread0.281 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations20
Published2020
Admission routes1
Has abstractyes

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