Indigenous Beadwork: Drawing Together Images from Indigenous Literature
Bibliographic record
Abstract
During the fall of 2022, I participated in an independent study in Indigenous Literatures at Mount Allison University. An important part of this study was examining different themes and images emphasized in the literature I read. I decided to express the knowledge I had gained through a beadwork project on a faux leather file holder (See image 1). Beading has been a way for me to feel more connected to my Indigeneity, and I am very honoured to be able to wear my beads and practice the art of beadwork as a Mi’kmaw woman. This artistic piece brings together some important themes and images from four Indigenous-authored texts: Cherie Dimaline’s The Marrow Thieves, Alicia Elliot’s A Mind Spread Out on the Ground, Thompson Highways, Kiss of the Fur Queen, and Thomas King’s The Truth about Stories: A Native Narrative. Together, these images tie together themes of Indigenous pride, love, resilience, community, and respect. The connectivity between the multiple books is reinforced by bringing these images together.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.025 | 0.024 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".