Sport Stadiums and Environmental Justice
Bibliographic record
Abstract
<p>This book explores the local environmental impact of sports stadiums, and how that impact can disproportionately affect communities of color. Offering a series of review articles and global case studies, it illustrates what happens when sport organizations and other public and private stakeholders fail to factor environmental justice into their planning and operations processes.</p><p>It opens with an historical account of environmental justice research and of research into sport and the natural environment. It then offers a series of case studies from around the world, including the United States, Canada, Kenya, South Africa, and Taiwan. These case studies are organized around key elements of environmental justice such as water and air pollution, displacement and gentrification, soil contamination, and transportation accessibility. They illustrate how major sports stadiums have contributed positively or negatively (or both) to the environmental health of the compact neighborhoods that surround them, to citizens’ quality of life, and in particular to communities that have historically been subjected to unjust and inequitable environmental policy. Placing the issue of environmental justice front and center leads to a more complete understanding of the relationship between stadiums, the natural environment, and urban communities.</p><p>Presenting new research with important implications for practice, this book is vital reading for anybody working in sport management, venue management, mega-event planning, environmental studies, sociology, geography, and urban and regional planning.</p><p>The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.</p>
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.001 |
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; both teacher heads agree on what is shown here.
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".