Betting on Reconciliation: Law, Self-Governance, and First Nations Economic Development in Canada
Bibliographic record
Abstract
Gaming Law Review and EconomicsVol. 15, No. 4 ArticlesBetting on Reconciliation: Law, Self-Governance, and First Nations Economic Development in CanadaPaul Seaman, Brenda Pritchard, and David PotterPaul SeamanSearch for more papers by this author, Brenda PritchardSearch for more papers by this author, and David PotterSearch for more papers by this authorPublished Online:4 May 2011https://doi.org/10.1089/glre.2011.15406AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetailsCited bySettler colonialism, Indigenous cultures, and the promotional landscape of tourism in Ontario, Canada's ‘near North’19 February 2019 | Journal of Heritage Tourism, Vol. 14, No. 3 Volume 15Issue 4Apr 2011 InformationCopyright 2011, Mary Ann Liebert, Inc.To cite this article:Paul Seaman, Brenda Pritchard, and David Potter.Betting on Reconciliation: Law, Self-Governance, and First Nations Economic Development in Canada.Gaming Law Review and Economics.Apr 2011.207-219.http://doi.org/10.1089/glre.2011.15406Published in Volume: 15 Issue 4: May 4, 2011PDF 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".