Reflections on the Business of Indians and Indian Business
Bibliographic record
Abstract
I begin with the question “why are Indians poor?” This reflects a common question that many Canadians ask themselves when presented with stark images of conditions on-reserve, Aboriginal protest, or the reality of homelessness and addiction in their city centres. There are many technical problems with the question as formulated; to begin with, all Indians are not poor. In some cases, the Indian band is poor, but an individual is not, and vice versa. It may be more interesting to ask why Indians are not prosperous, or when are Indians not poor? What are the community and individual factors that contribute to what situations, and what is the outlook for Canada given the current demographic projections of the Aboriginal population and socioeconomic outcomes? The proposed collaborative strategy is a transformation which requires an examination of everything that we do as business, as government and as individuals, beginning with prioritizing equality and inclusion of First Nations in Canada.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.042 | 0.026 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".