Bibliographic record
Abstract
While new scandals coming to light weekly, the growth of online sports betting hasn’t slowed. There are still new markets and opportunities to explore, including women’s and youth sports. In fact, it is difficult to imagine who could ever disrupt this wildly successful industry – except, of course, an industry that’s figured out a way around all the rules and regulations. Features excerpts of interviews with: Cole Wogoman, Senior Manager, Government Relations and League Partnerships National Council on Problem Gambling Ilya Beylin, Associate Professor of Law Seton Hall Law School Legal Materials Referenced: Central Hudson Gas & Elec. v. Public Svc. Comm’n, 447 U.S. 557 (1980) 44 Liquormart, Inc. v. Rhode Island, 517 U.S. 484 (1996) Kan. Stat. Ann. § 74-8785 Conn. Gen. Stat. Ann. § 12-863 N.C. Gen. Stat. Ann. § 18C-910 17 CFR § 40.11 Event Contracts, 89 Fed. Reg. 48968 CFTC, Rel. No. 9137-25 (Sept. 30, 2025), CFTC Staff Issues Advisory on Certain Contract Markets KalshiEx v. Hendrick, No. 2:25-cv-575 (D. Nev.) KalshiEx v. Flaherty, No. 1:25-cv-2152 (D.N.J.) Robinhood Derivatives v. Dreitzer, No. 2:25-cv-01541 (D. Nev.) Robinhood Derivatives v. Flaherty, No. 1:25-cv-14723 (D.N.J.) Robinhood v. Campbell, No. 1:25-cv-12578 (D. Ma.) Cited Articles: Ilya Beylin, Event Contracts Are a Step Too Far for Derivatives Regulation Ellen McGrane et al., What is the Impact of Sports-Related Gambling Advertising on Gambling Behaviour? A Systematic Review Andrew Kim and John Servidio, On the Line: The Legality of Sports Prediction Markets Will Soon Be Tested Andrew Kim, The Hitchhiker’s Guide To Prediction Markets Litigation Other Sources: 2024 Sports Betting Advertising Trends, American Gaming Association Canadian Centre on Substance Use and Addiction, Gambling Availability and Advertising in Canada
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.133 | 0.055 |
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".