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Record W2321907794

Are Canadian First Nations Casinos Providing Maximum Benefits? Appraising First Nations Casinos in Ontario, Saskatchewan, and Alberta, 2006-2010(1)

2014· article· en· W2321907794 on OpenAlexaboutno aff
Yale D. Belanger

Bibliographic record

VenueGaming research & review journal · 2014
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueProfit (economics)BusinessEconomic growthEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

AbstractTo date a dearth of data has made it difficult to evaluate the success of First Nations casinos in Canada. This paper helps remedy this situation by presenting a three-province overview (Ontario, Saskatchewan and Alberta) of First Nations gaming models. Two key findings are offered that First Nations seeking gaming market entry and provincial officials should genuinely consider. First, while each province has adopted a unique approach to First Nations gaming policy they have each opted to direct substantial revenues out of First Nations communities and into their own treasuries. Second, the evidence suggests that larger gaming properties located nearby a significant market provide more benefits versus smaller properties situated in more isolated areas. The subsequent discussion elaborates each provincial model's revenue generating power, how the revenue in question is being allocated and its corresponding socio-economic impact, whether increased problem gambling and crime have resulted as predicted, while exploring employment trends to determine whether they have developed as anticipated.IntroductionEvaluating the impact of First Nations casinos in Canada has been hampered by a lack of data (cf Cornell, 2008).2 First Nations leaders in several provinces are nevertheless considering investing in reserve casino expansion. There are currently 17 First Nations casinos operating nationally in B.C. (1), Alberta (5), Saskatchewan (6), Manitoba (2) and Ontario (1 for profit; 2 charity). Initially touted as revenue generators that would employ large numbers of Aboriginal employees thus increasing community benefits, provincial premiers in Ontario, Saskatchewan and Alberta echoed the First Nation leadership's positive testimonials to likewise champion reserve casino expansion as a means of improving local First Nations development potential and well-being. Each provincial government chose, however, to execute policies prescribing revenue distribution formulas, restrictions on casino site construction, while most importantly directing casino revenues purportedly earmarked for First Nations communities into provincial treasuries. Despite these setbacks, First Nations leaders in each province remain confident in the reserve casino's potential. Aside from assenting, anecdotal declarations, modest efforts have been directed at exploring each model's exigencies in comparative perspective. This paper represents the first multi-province evaluation of First Nations casinos in Canada and assesses the Ontario, Saskatchewan, and Alberta First Nations gaming models to determine whether First Nations casinos are providing maximum benefits.3 In particular, the following discussion evaluates the three provincial gaming models' revenue generating power, how the revenue is being allocated and its corresponding socio-economic impact, whether increased problem gambling and crime have resulted as projected, while exploring employment trends to determine their impact. This paper unfolds as follows. After a literature review an overview of each provincial First Nations gaming policy is provided followed by a quantitative analysis exploring the aforementioned subjects, and the conclusions.Literature ReviewUnited States Indian nations started utilizing reservation casinos as an economic stimulus in the 1980s. Described as islands of poverty in a sea of wealth (Anderson & Parker, 2008, p. 641), as one researcher has noted, reservation communities [w]ith little or no economy or tax base to fund essential services ... turned to gaming, through self-determination, to generate government revenue needed to fund these services and provide employment for tribal (Schaap, 2010, p. 381). By 2002, over half of all tribal members living in the contiguous 48 states belonged to casino-operating tribes (Evans & Topoleski, 2003) and, in 2008, Indian gaming revenues topped $26.7 billion with 233 Indian tribes operating 411 casinos, bingo halls, and pull-tab operations in 28 states (NIGA, 2009). …

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.161
GPT teacher head0.397
Teacher spread0.235 · 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 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

Citations4
Published2014
Admission routes1
Has abstractyes

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