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Record W4365814673 · doi:10.15402/esj.v8i4.70802

We are the Salmon Family: Inviting Reciprocal and Respectful Pedagogical Encounters With The Land

2023· article· en· W4365814673 on OpenAlexaffvenue
Cher Hill, Neva Whintors, R. Bailey

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsAssembly of First NationsSimon Fraser University
Fundersnot available
KeywordsIndigenousReciprocalParticipatory action researchEmbodied cognitionAction (physics)Environmental ethicsCitizen journalismSociologyEcologyPolitical scienceAnthropologyEpistemologyLaw

Abstract

fetched live from OpenAlex

Through this action research project, we endeavour to reconfigure pedagogical encounters involving children and the natural world to be more reciprocal and respectful, as well as responsive to the ecological crisis. The goal of our research is to advance understandings of how to educate children to become good relatives to all the beings on these Lands. We are guided by the question: How can we educate children to live like Salmon People (those Indigenous to this place), which is the sacred responsibility of all those residing on the Coast Salish territories? Practices that contributed to shifting relationship between people and the Land and moved our community beyond our human-centric engagement were participatory and embodied. They included acts to care for Salmon and other beings as relatives, as well as experiencing Land as agential and existing independently of human desire. We see our research as a site for what Kari Grain calls “critical hope.”

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)0.017
metaresearch head score (Gemma)0.019
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 score0.025
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0250.032
Scholarly communication0.0090.010
Open science0.0030.019
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.250
GPT teacher head0.430
Teacher spread0.180 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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