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Record W4385787408 · doi:10.5194/ica-abs-6-255-2023

Storytelling, Visual and Cognitive Mapping and Photoatlassing of Indigenous Elder William Commanda’s Canoe Journey: Art, Craft, Motion, Experience, Knowledge and Wisdom

2023· article· en· W4385787408 on OpenAlexaff
Romola V. Thumbadoo, D. R. Fraser Taylor, Alexander Wolodtschenko

Bibliographic record

VenueAbstracts of the ICA · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsCarleton University
Fundersnot available
KeywordsCraftStorytellingMotion (physics)IndigenousVisual artsCognitionArtPsychologyNarrativeSociologyArtificial intelligenceComputer scienceLiterature

Abstract

fetched live from OpenAlex

This presentation examines the importance of the motif of the Indigenous canoe in the integration of art, craft, cybernetic and cognitive cartography, ecology, movement, epistemology and relationality in the life and work of North American Indigenous Elder William Commanda, founder of the Circle of All Nations. A world-renowned canoe builder and environmentalist, Law of Nature preoccupations featured in his life-long exploration of canoe as material object and epistemological methodological tool, to present ephemeral Indigenous relational and bridge building knowledge and wisdom. His formal outreach to the mainstream world of North America is documented in a 1960s video of the Smithsonian Institute; in Denmark, in commemorative photo/teaching books at the Roskilde Museum and, at age 90, in the Good Enough for Two canoe-making documentary (2006). Today, there is an explosion of interest in the canoe in school and museum settings in Canada, and the spatial and temporal significance of this iconic emblem utilizes and is complemented by semiotic Photoatlas orientation and analysis (after Wolodtschenko).

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.034
GPT teacher head0.264
Teacher spread0.230 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueAbstracts of the ICASame topicMuseums and Cultural HeritageFrench-language works237,207