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Record W4388025243 · doi:10.3828/bjcs.2023.12

Indigenous artefacts and oral stories

2023· article· en· W4388025243 on OpenAlexaboutno aff
Arzu Sardarli

Bibliographic record

VenueBritish Journal of Canadian Studies · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousArchaeologyWork (physics)Library scienceGeographyEngineeringEcologyComputer scienceBiology

Abstract

fetched live from OpenAlex

This article presents the results of studies conducted by Canadian academics in collaboration with Sturgeon Lake and Pelican Narrows First Nations communities (Saskatchewan, Canada). The objectives of the project were: (1) developing a research ethics protocol for collecting, studying, and preserving indigenous artefacts; (2) measurements of chemical compositions of artefacts; (3) collecting oral stories of Elders. Within the project, two workshops were organized in Pelican Narrows and Sturgeon Lake. Post-secondary students were trained to work on the project. The laboratory measurements of chemical compositions of artefacts were conducted at the Scanning Electron Microscope Laboratory (University of Alberta), and the Saskatchewan Isotope Laboratory (University of Saskatchewan). The carbon-dating measurements were carried out at André E. Lalonde Accelerator Mass Spectrometry Laboratory (University of Ottawa). The statistical analysis of chemical compositions was conducted in order to test the provenance similarities of artefacts. The project was supported by the Department of Canadian Heritage.

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)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.750

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.005
Science and technology studies0.0160.019
Scholarly communication0.0110.003
Open science0.0020.012
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.029
GPT teacher head0.224
Teacher spread0.194 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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