Bibliographic record
Abstract
Mining operations present social, economic, and environmental benefits and challenges, many of which are context specific. While social approaches and spatial approaches have been used to study mining impacts for decades, approaches that consider social and spatial dimensions in tandem are growing. This is timely from a sustainability perspective given the need for integrated research approaches that can uncover the nature of complex mining-related challenges and deliver effective solutions. This paper presents findings from a systematic literature review. It documents concepts and methods used to frame and investigate social space to date within mining contexts and considers how these link to sustainability. This study finds that social spatial research on mining is framed primarily by socio-spatial, socio-ecological, and materialist perspectives. Authors mainly rely on traditional methodologies and methods, especially ethnography. Social spatial research appears to be well-suited to the study of diverse relational dynamics in the context of mining and sustainability. However, while existing research has contributed to much new knowledge about complex sustainability problems (systems knowledge) and values that ought to change (target knowledge), fewer papers consider strategies for addressing these problems (transformation knowledge). Future research might adopt co-productive and/or transdisciplinary approaches to develop new, innovative research methods and meaningful solutions to sustainability challenges.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".