Games of Skill and Chance in Canada
Bibliographic record
Abstract
Gaming Law ReviewVol. 9, No. 1 Original PapersGames of Skill and Chance in CanadaMichael D. Lipton, Morden C. Lazarus, and Kevin J. WeberMichael D. LiptonSearch for more papers by this author, Morden C. LazarusSearch for more papers by this author, and Kevin J. WeberSearch for more papers by this authorPublished Online:17 Feb 2005https://doi.org/10.1089/glr.2005.9.10AboutSectionsPDF/EPUB ToolsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Volume 9Issue 1Feb 2005 InformationCopyright 2005, Mary Ann Liebert, Inc.To cite this article:Michael D. Lipton, Morden C. Lazarus, and Kevin J. Weber.Games of Skill and Chance in Canada.Gaming Law Review.Feb 2005.10-18.http://doi.org/10.1089/glr.2005.9.10Published in Volume: 9 Issue 1: February 17, 2005PDF download
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".