Bibliographic record
Abstract
Wanda WuttuneeMore than 500 people attended the Tribal Governance Symposium: Compacts, Contracts and Agreements which has held in March, 2001 at the University of Oklahoma.The main topic was gaming and speakers from across the states addressed issues ranging from jurisdiction to testimonials from groups involved in gaming in the state of Oklahoma.I would like to share the perspectives of one of the presenters, Kevin Gover.The rest of the relevant American material is covered extensively across the articles from Lessons of Research in this issue.Kevin asked questions that every individual and community might consider regularly as it determines the kind of community and the type of activities it wants to support.These are governance issues no less.Kevin is an Indian ("Aboriginal person" in Canada) lawyer with extensive experience in federal law relating to Indians and Indian Tribal law.He was the former Assistant Secretary for Indian Affairs and has testified extensively before Congress.He has written and spoken on issues of law and policy involving Indian tribes.Kevin asked questions about our image as Indians within society and the kind of communities that we want to have.These kinds of questions demand time, discussion and personal reflection before they can be adequately addressed.Yet they are critical because they
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.042 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".