Preserving a Settlement despite Ongoing Challenges: The Case of Native Indian Gaming
Bibliographic record
Abstract
Abstract We investigated how an institutional settlement concerning Native Indian gaming (the operation of gambling establishments such as casinos or bingo halls by Native Indian tribes) was preserved over time in spite of three significant challenges. Building on previous literature on settlements and institutional logics, we see settlements as institutional arrangements that manage power dynamics and competing institutional logics. Based on our analyses of the settlement and three challenges in the Native gaming field, we suggest that even seemingly volatile institutional settlements can be maintained when powerful actors balance each other’s ability to modify the settlement and different actors invoke alternative institutional logic(s). We also find that these processes can be facilitated by the embeddedness and formality of the settlement. We contribute to the settlement literature by showing how settlements can be maintained when actors draw on equally strong sources of power and different logics to counter the actions of other actors. Furthermore, we shed light on “how institutions matter” by demonstrating how institutional settlements can facilitate field stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.028 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".