Inking of Immunity Episode 8. Indigenous Tattoo Revival with Dion Kaszas
Bibliographic record
Abstract
Dion Kaszas is an Nlaka’pamux cultural tattoo practitioner and a leader in the revival of Indigenous tattooing in Canada. He has been tattooing professionally since 2009 and started the revival of Nlaka’pamux tattooing in 2012. Dion travels to National and International events, conferences, and tattoo festivals representing Indigenous tattooing in Canada. Dion's passion for tattooing extends beyond his artistic work into the successful completion of his Masters degree in Indigenous Studies at the University of British Columbia Okanagan. His continued area of research is Indigenous tattooing, focusing keenly on the revival of Indigenous peoples tattooing practices, using Indigenous and Creative research methodologies. Since his graduation Dion has contributed to a variety of publications as author and editor. His work has been featured in Spiritual Skin: Magical Tattoos and Scarification, Tattoo Traditions of Native North America: Ancient and Contemporary Expressions of Identity, The World Atlas of Tattoo, and highlighted in Newspaper articles from the New Zealand Herald to the CBC. In 2018, he was featured in Skindigenous, a 13-part documentary series produced in association with APTN exploring Indigenous tattooing traditions around the world. Dion is a recipient of a Long Term Project grant through the Creating, Knowing and Sharing: The Arts and Cultures of First Nations, Inuit and Métis Peoples component of the Canada Council for the Arts, for his project “Taking Nlaka’pamux Tattooing to the World.” Dion acknowledges the support of the Canada Council for the Arts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".