Studies of Physical Parameters of Indigenous Artifacts. Collecting and Preserving the Relating Oral Stories
Bibliographic record
Abstract
The project, supported by the Department of Canadian Heritage, was conducted by scholars from First Nations University of Canada (FNUniv), University of Regina (U of R) and Royal Saskatchewan Museum (RSM) in 2018 - 2020, in collaboration with Sturgeon Lake and Pelican Narrows First Nations communities. More than forty people, including Elders and students, participated in this project. The research Ethics review was done by U of R. We also consulted with Elders regarding the research Ethics protocols during the individual meetings, ceremonies and workshops in Pelican Narrows and Sturgeon Lake. Research assistants (Indigenous students) were trained (Indigenous studies and the basics of archaeology) for working in First Nations communities and at the RSM. We interviewed Elders and Knowledge Keepers from Pelican Narrows and Sturgeon Lake and recorded their oral stories; collected Indigenous artifacts in these communities and selected samples from RSM collections for physical measurements at the Scanning Electron Microscope Laboratory of the University of Alberta, Saskatchewan Isotope Laboratory of the University of Saskatchewan and André E. Lalonde Accelerator Mass Spectrometry Laboratory of the University of Ottawa. Then we carried out the statistical analysis of the obtained data. The preliminary results of the project were presented to community members.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".