DARK SIDE CASE: Orange Shirt Day: Exploitation of Indigenous Peoples and Appropriation of Culture
Bibliographic record
Abstract
At just six years old, in 1973, Phyllis Webstad was filled with excitement to go to the Mission School near Williams Lake, British Columbia. Living on Dog Creek Reserve — Stswecem’c Xgat’tem First Nation, her grandmother had intentionally saved up in order to gift her a shiny new orange shirt for her first day of school. It was never to be seen again. Since then, the colour orange has reminded Phyllis of that moment when her feelings did not matter, and her worth was nothing. Years later she reflects on how those initial feelings of worthlessness and significance affected her life. On September 30, 2013, Phyllis spoke about her experience at The Mission School, starting the Orange Shirt Day movement. The colour orange is associated with the stolen lives of Indigenous children who were forced into residential schools. However, for Indigenous communities it is a symbol of solidarity, healing, and allyship. September 30, 2021 was what we now call “Canada’s”, first National Truth and Reconciliation Day. Orange shirts were worn across the country, signifying emotions from sorrow to strength, unity, and defiance. The act of solidarity was jaded by the finding of that many Indigenous artists’ intellectual property was stolen and used without permission for commercial gain. As the importance of the day grows what can be done to prevent further exploitation?
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".