A Research Note on the Impact of the Economic Recession on Indian Gaming in Connecticut
Bibliographic record
Abstract
The economic recession that began in the fall of 2007 has tested the durability of the gaming industry in the United States. According to a 2009 “State of the States” report released by the American Gaming Association (AGA), gross gaming revenues nationwide were down 4.7% from 2007, while the New York Times reports that 60% of gaming patrons across the country have cut back their spending on casino gambling (Urbina 2009). Discourse regarding the overall impact of the economic recession on Indian gaming has been less definite. Both the National Indian Gaming Association (NIGA) and the National Indian Gaming Commission (NIGC) have reported that, while revenues from Indian gaming have slowed over the past year, the industry remains relatively unaffected by the economic downturn overall (NIGA 2009; NIGC 2009). The NIGC reports that Indian gaming industry’s gross revenues in 2008 exceeded revenues in 2007 by approximately $500 million, translating into a 2.3% increase since the dawn of the recession (NIGC 2009). However, this information comes alongside news surfacing across Indian Country of declines in net win (Palermo 2009), dips in discretionary spending (Toensing 2009; Urbina 2009), layoffs and salary cuts of casino employees (Light and Rand 2009; Salgado 2009), and even casino closures (Palermo 2009). In a research note appearing in the Spring 2009 issue of this journal, we reported that the tribes with Class III gaming in the state of New Mexico had experienced a steady decline in net win revenues since the first quarter of 2008, with further losses anticipated in 2009. Thus, while the resiliency of the Indian gaming industry may be substantiated at the national level, there appears to be considerable variation in regards to the impact of the recession at the tribal level that continues to deserve attention.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".