Indigenous People in the Global Context
Bibliographic record
Abstract
There are 350 million indigenous people in the world, all are in a similar circumstance. They are still classically colonized, robbed of their territory and live on the periphery of a globalized imperial economy that is threatening the globe. Generally speaking the Indigenous people have been “dumbed down” to a pre-civilized state. It is generally agreed that Indigenous people were non-scientific, non-theoretical, incapable of abstraction and so forth. In fact, science is just now catching up to some key understandings that Indigenous people have had for a very long time. For the most part, Indigenous people are oral and therefore cannot be believed, nor studied by western intellectuals. Why is this a problem? About the Lecturer: Ms. Maracle is the author of a number of critically acclaimed literary works including: Ravensong [novel], Canadian Scholar’s Press, Bobbi Lee [autobiographical novel], Three O’clock Press, Daughters Are Forever, [novel] Theytus Will’s Garden [young adult novel] Theytus books, “Bent Box” [poetry] Theytus books, “I Am Woman” [non-fiction], Polestar/Raincoast and the co-editor of a number of anthologies including the award winning publication, “My Home As I Remember” [anthology] Natural Heritage books. Ms. Maracle is widely published in anthologies and scholarly journals worldwide. Ms. Maracle is a member of the Sto: Loh nation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".