Temporary Regulations and Institutional Change: Insights from the Brazilian World Cup Experience
Bibliographic record
Abstract
While institutional theory has extensively examined how durable regulations contribute to institutional change, little is known about how temporary regulations—pervasive in modern societies—can transform institutions. This study examines how and why temporary regulations can lead to lasting institutional change by analyzing Brazil’s temporary legalization of beer sales and consumption in stadiums during the 2014 FIFA World Cup. Using extensive qualitative data, we develop a process model explaining how the introduction of a temporary regulation creates an opportunity for society to experience a counter-normative practice and learn from it. During this period, multiple institutional actors—while pursuing objectives far removed from considerations of institutional change—perform various types of institutional work to contain the practice’s expected negative outcomes. This institutional work unintendedly generates new societal understandings about the practice’s risks and consequences, which interested actors then leverage to achieve lasting institutional change. We advance institutional theory by identifying an unexplored path of institutional change through temporary regulations, demonstrating how temporary regulations can enable practice variations and institutional learning, and revealing how institutional maintenance work can drive change. Our insights suggest that temporary regulations can be valuable policy tools for reducing uncertainty and enabling learning about alternative institutional arrangements.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".