Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it