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Gambling and Sport

2018· other· en· W4243445319 on OpenAlexaff
Garry J. Smith

Bibliographic record

VenueThe Blackwell Encyclopedia of Sociology · 2018
Typeother
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHorse racingLeagueAdvertisingEliteFantasyPoint (geometry)BusinessPolitical scienceLawEntertainmentComputer sciencePolitics

Abstract

fetched live from OpenAlex

Abstract This entry outlines the current global sports‐betting landscape and traces the history of sports‐betting from ancient to modern times. Horse‐racing has long been a popular gambling format, due to the thrill of watching elite equine talent perform and because of technological advancements such as the pari‐mutuel betting system that allows on‐track spectators to view the money bet on each horse and the grand total in the betting pool in real time. The creation of the “point spread,” extensive television coverage, and sports leagues expanding from coast to coast have all spurred an interest in betting on team sports, which has led to a decline in horse‐race wagering. Both legal and illegal sports‐betting are discussed, along with the dearth of legal sports‐betting outlets in North America. Newer sports‐gambling modes such as online betting via offshore Web sites and daily fantasy sports contests are described, and the entry concludes with a discussion on the possibility of changing sports‐betting laws to make the activity more widely available.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.048
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0480.004

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.

Opus teacher head0.052
GPT teacher head0.363
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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