A methodology for pre-selecting sustainable development indicators (SDIs) with application to surface mining operations
Bibliographic record
Abstract
The creation of the World Commission on Environment and Development (WCED), commonly known as the Brundtland Commission, and the publication in 1987 of its report, "Our Common Future" marked a turning point towards finding the balance among society, economy, and environment.Since then, governments have improved existing regulations or created others, organizations for standardizations have developed new standards, management and process practices have addressed potential gaps, public and private organizations have taken initiative through the creation of committees and programs and research covering all areas of sustainable development has become a priority for academics and practitioners.These different sources serve as the basis for a pre-selection process of sustainable development indicators (SDIs).While some sources do not specifically address certain industries, the pre-selection process suggested in this manuscript studies and analyzes each SDI's resource and the possible applicability of already-identified indicators.An assertive set of SDIs is not solely based on regulatory systems, as measuring sustainability cannot become a bureaucratic process, and neither can any other SDI's source single-handedly determine or mandate the final set of indicators, as the real objective is to assist decision-makers and effectively engage stakeholders.This paper presents an analysis of six different sources for pre-selecting SDIs, accompanied by a methodology to then finalize with a set of SDIs for the surface mining operations in oil sands projects.Surface mining projects are complex operations with several social, economic, environmental, and health impacts.As the government and oil sands developers are turning towards increasing Environmental Impact II 519 www.witpress.com,
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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 | 0.039 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".