MétaCan
Menu
Back to cohort
Record W4411095129 · doi:10.1080/14459795.2025.2512931

Media portrayal of sports betting in Canada before and after Federal Bill C-218

2025· article· en· W4411095129 on OpenAlexafffundabout
Paul William Weston, Luke Clark

Bibliographic record

VenueInternational Gambling Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdvertisingHorse racingPsychologyPolitical scienceEconomicsBusinessLaw

Abstract

fetched live from OpenAlex

In 2021, Federal Bill C-218 allowed the legalization of single-event sports betting in Canada. The rationale and repercussions of Bill C-218 received substantial media coverage. We sought to characterize the themes and voices that were present in Canadian newspaper coverage of sports betting, using the Canadian Newsstream database to identify print articles published in two time periods, before (Jan 2020–June 2021) and after (July 2021–Dec 2022) the bill was passed. We coded articles for seven main themes, associated subthemes, and voices. In 146 articles, dominant themes were Legality (85.6%) and Industry Change (83.6%). Although Technology (52.7%) was well represented, discussion of In-Play Betting as a subtheme was coded in only 21.9% of articles. Gambling Harm and Reform were less represented, in less than a quarter of articles. In terms of voices, Industry representatives (70.5%) were most frequent. Few articles featured voices of Academics, Treatment Providers/NGOs, and people with Lived Experience of gambling harms. We argue that the Canadian media coverage of the legalization of sports betting has emphasized the corporate and economic impacts, with less attention to the risks of harm associated with the expansion of sports betting, and changes to the underlying gambling product.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.341
Teacher spread0.318 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueInternational Gambling StudiesSame topicDoping in SportsFrench-language works237,207