A review of brownfields revitalisation and reuse research in the US over three decades
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it