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Record W407326877 · doi:10.3138/9781442621008

At Odds: Gambling and Canadians, 1919-1969

2003· book· en· W407326877 on OpenAlexaboutno aff
Suzanne Morton

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2003
Typebook
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsProscriptionDecriminalizationLegislationSociologyLegalizationSecularizationProtestantismState (computer science)Political scienceGender studiesCriminologyLawSocial sciencePolitics

Abstract

fetched live from OpenAlex

Using a rich variety of historical sources, Suzanne Morton traces the history of gambling regulation in five Canadian provinces - Nova Scotia, Quebec, Ontario, Manitoba, and B.C. - from the First World War to the federal legalization in 1969. This regulatory legislation, designed to control gambling, ended a long period of paradox and pretence during which gambling was common, but still illegal. Morton skilfully shows the relationship between gambling and the wider social mores of the time, as evinced by labour, governance, and the regulation of 'vice.' Her focus on the ways in which race, class, and gender structured the meaning of gambling underpins and illuminates the historical data she presents. She shows, for example, as Old Canada (the Protestant, Anglo-Celtic establishment) declined in influence, gambling took on a less deviant connotation - a process that continued as charity became secularized and gambling became a lucrative fundraising activity eventually linked to the welfare state. At Odds is the first Canadian historical examination of gambling, a complex topic which is still met by moral ambivalence, legal proscription, and volatile opinion. This highly original study will be of interest to the undergraduate history or social science student, but will also hold the attention of a more general reader.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0340.003
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.215
Teacher spread0.194 · 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.

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

Citations25
Published2003
Admission routes1
Has abstractyes

Explore more

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