Integrating the Social Dimension in Remediation Decision‐Making: State of the Practice and Way Forward
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
Sustainable remediation guidance, frameworks, and case studies have been published at an international level illustrating established sustainability assessment methodologies and successful implementation. Though the terminology and indicators evaluated may differ, one common theme among international organizations and regulatory bodies is more comprehensive and transparent methods are needed to evaluate the social sphere of sustainable remediation. Based on a literature review and stakeholder input, this paper focused on three main areas: (1) status quo of how the social element of sustainable remediation is assessed among various countries and organizations; (2) methodologies to quantitatively and qualitatively evaluate societal impacts; and (3) findings from this research, including challenges, obstacles, and a path forward. In conclusion, several existing social impact assessment techniques are readily available for use by the remediation community, including rating and scoring system evaluations, enhanced cost benefit analysis, surveys/interviews, social network analysis, and multicriteria decision analysis. In addition, a list of 10 main social indicator categories were developed: health and safety, economic stimulation, stakeholder collaboration, benefits community at large, alleviate undesirable community impacts, equality issues, value of ecosystem services and natural resources, risk‐based land management and remedial solutions, regional and global societal impacts, and contributions to other policies. Evaluation of the social element of remedial activities is not without challenges and knowledge gaps. Identification of obstacles and gaps during the project planning process is essential to defining sustainability objectives and choosing the appropriate tool and methodology to conduct an assessment. Challenges identified include meaningful stakeholder engagement, risk perception of stakeholders, and trade‐offs among the various triple bottom line dimensions. ©2015 Wiley Periodicals, Inc.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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