Bibliographic record
Abstract
In this Lessons from Experience section we included two case studies -on Kitsaki Management Ltd.Partnership, LaRonge, SK, and Cameco Corporation -that build on conference presentations by Raymond A. McKay, CEO of Kitsaki, and Jamie McIntyre, Director of Sustainable Development and Corporate Relations, Cameco.Raymond McKay shares the success story of Kitsaki, the business arm of the Lac La Ronge Indian Band, which has become a key player in the economic life of northern Saskatchewan by remaining true to its philosophy on resources, treasuring the Land as a heritage resource for future generations.The Kitsaki Model for secure and sustainable development has three key components: developing a diverse network of profitable enterprises with proven partners, maximizing Aboriginal employment, and maintaining and supporting Traditional Aboriginal Knowledge.The case study of Cameco, on whose board sits Chief Harry Cook of the Lac La Ronge Indian Band, offers Cameco's own models for success, tracing the challenges and opportunities of Aboriginal business and community partnerships.The case makes clear the value of nourishing corporate and community mindsets, designing clear procedural guidelines and policy frameworks, ensuring management commitment, and securing buy-in through flexibility and strong communications.Kenneth W. Tourand's essay reflects on the experience with unionization of one Aboriginal organization trying to maintain traditional values and culture in a time of organizational change.With a newly certified trade union, the Nicola Valley Institute of Technology (NVIT) risked eroding its status as an Aboriginal post-secondary
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.270 | 0.176 |
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".