Involving stakeholders to achieve successful development of brownfield sites
Bibliographic record
Abstract
Our overall quality of life depends on balancing the interrelationship between human and ecological health, socio-cultural values, and economic well-being. Achieving appropriate balance of these components is critical to modern enviromnental (e.g., Brownfield site) decision-making. The American Society of Testing and Materials (ASTM) is involved in developing a standard guide to facilitate the analysis and management of Quality of Life decision making. This guide will provide a process to help identify, analyze, and resolve stakeholders’ issues associated with environmental problems, A key component to the Quality of Life process is to empower the affected stakeholders to enable genuine participation in the decision making and management process. The basic components of the Quality of Life process will be presented along with an example case where the methods have been applied successfully to the development of a Brownfield property in urban Toronto, Canada. The application of the Quality of Life process enabled participation of all the affected stakeholders (people in the community, the developer, local government and regulators) from the very beginning. The stakeholders participated in all decision-making of the redevelopment process; from planning the types and locations of buildings through landscapinglcommunity art for the site, traffic flowlpublic transportation, day-care requirements and a variety of specific community amenities (up-grading lake access portals, various water recreation facilities, community playground equipment). Application of the Quality of Life process resulted in a win-win situation for all stakeholders (i.e., people in the community, regulators and the developer). The derelict industrial property is being replaced by a residential development that will improve the overall quality
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".