The Need for the Creation of a Framework for Aboriginal Gaming to Legally Exist in Canada: The Compelling Case of the Mohawks of Kahnawá:ke
Bibliographic record
Abstract
Gaming Law Review and EconomicsVol. 14, No. 2 ARTICLESThe Need for the Creation of a Framework for Aboriginal Gaming to Legally Exist in Canada: The Compelling Case of the Mohawks of Kahnawá:keMorden C. Lazarus and Brian HallMorden C. LazarusSearch for more papers by this author and Brian HallSearch for more papers by this authorPublished Online:5 May 2010https://doi.org/10.1089/glre.2010.14205AboutSectionsPDF/EPUB ToolsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetailsCited byANZSOC 23rd Annual Conference, Alice Springs, September, 2010. Aboriginal justice issues – trying for new approaches, while clinging to the old: Our shared experiences18 August 2011 | Australian & New Zealand Journal of Criminology, Vol. 44, No. 2 Volume 14Issue 2Mar 2010 Information© 2010 Mary Ann Liebert, Inc.To cite this article:Morden C. Lazarus and Brian Hall.The Need for the Creation of a Framework for Aboriginal Gaming to Legally Exist in Canada: The Compelling Case of the Mohawks of Kahnawá:ke.Gaming Law Review and Economics.Mar 2010.95-99.http://doi.org/10.1089/glre.2010.14205Published in Volume: 14 Issue 2: May 5, 2010PDF download
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.023 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".