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Record W4386793517 · doi:10.3390/arts12050198

Knowledge Repatriation: A Pilot Project about Making Cedar Root Baskets

2023· article· en· W4386793517 on OpenAlexaffabout
Sharon M. Fortney

Bibliographic record

VenueArts · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsRoyal British Columbia Museum
Fundersnot available
KeywordsRepatriationIndigenousTraditional knowledgeWork (physics)UrbanizationPolitical scienceGeographySociologyArchaeologyEthnologyEngineeringEconomic growthEcology

Abstract

fetched live from OpenAlex

This paper describes the first phase of a Coast Salish Knowledge Repatriation Project being coordinated by the Curator of Indigenous Collections and Engagement at the Museum of Vancouver, within the unceded, ancestral territories of the xʷməθkʷəýəm (Musqueam), Sḵwx̱wú7mesh (Squamish), and səlilwətaɬ (Tsleil-Waututh) nations. The goal of this knowledge repatriation work is to support cultural revitalization and language renewal through activities that generate learning opportunities for community members. These activities pivot around knowledge that has been lost due to urbanization, forced assimilation efforts, and other colonial activities that may have restricted access to traditional lands and resources, preventing knowledge transmission. This work is about shifting the focus from extractive projects, that benefit external audiences, to one that supports capacity building and cultural renewal within communities. This essay describes a project to reintroduce coiled cedar root basketry into communities within the Greater Vancouver area in the province of British Columbia, Canada.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.006
Scholarly communication0.0040.002
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.074
GPT teacher head0.391
Teacher spread0.317 · 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
Published2023
Admission routes2
Has abstractyes

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