A review of brownfields revitalisation and reuse research in the US over three decades
Bibliographic record
Abstract
Over the past 30 years, US-based research on contaminated and potentially-contaminated sites, or brownfields, has grown from defining the scope and size of the environmental, health and economic risks posed by abandoned manufacturing sites to exploring and documenting site-specific and area-wide impacts of their cleanup and revitalisation. From early and varied research on environmental and economic policy to equity and public impacts on minority communities, later research considered planning, adding case studies on sustainability and resilience to the scope of research covered. This review paper stems from exchanges of a long-standing network of academic, government agency, and practice professionals working to identify research, policy, and practice gaps. It traces the evolution of US brownfield revitalization research as was informed by, and informed, policy, program and practice. This review summarizes the literature and identifies research gaps and opportunities to further community and agency actions related to investigating, remediating, and redeveloping brownfield sites. It outlines site and area options to build climate resilience, strengthen community action for dismantling structural racism and disinvestment, and reduce the disproportionate risks experienced by communities of colour and areas of low income. The authors propose a new research agenda to address the gaps identified.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".