The Saskatchewan Indian Gaming Authority’s Approach To Securing Public Trust, 2000–2004
Bibliographic record
Abstract
The effects of the SIGA scandal, or the "Dutch Lerat Affair," as it was branded, led many to publicly question SIGA's accountability, which potentially undermined its corporate image. Since a corporation's image is the link between corporate reality and public perception, how people view a company is vital to that company's success. In 1993, the FSIN approached Premier Roy Romanow (NDP) to discuss reserve casino construction. Since taking the reins in 1991, Premier Romanow had been considered pro-business and compassionate towards First Nations issues, leading Chief Roland Crowe to comment, "This historical relationship meant that the Native leadership felt comfortable initiating a discussion regarding a Native casino gambling policy with the NDP government, which demonstrated an impressive level of trust in the Romanow government" (Skea, 1997, p. 103). Seeking to establish a working relationship with the province that would lead to new gaming policies benefiting its member communities, the FSIN cited a corresponding desire to stimulate economic development. Each CDC was established to aid in distributing one-quarter of the net profit share pursuant to the Framework Agreement in an effort to (i) stimulate First Nations economic development; (ii) fund reserve justice and health initiatives; (iii) finance reserve education and cultural development; (iv) improve community infrastructure; and (v) develop senior and youth programs and other charitable purposes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.018 | 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".