Hybridizing Sustainability Metric Applications with Local Stakeholder Inputs: A life cycle assessment with a co-design demonstration
Bibliographic record
Abstract
Sustainability standards have been criticized as being complex and overlapping, with unclear metrics and messy timelines—all of which have led to sustainability shortfalls. As part of the special issue on addressing sustainability metric shortcomings, we develop a hybridizing protocol for sustainability standards that elicits community stakeholder ideological predispositions, preferences, ratings, and heuristics and injects them into a prioritized, vetted metric in order to reduce immediate and longer-range life cycle impacts of corporate operations in the local ecosystem. We demonstrate our method using a quasi-field experiment, conducted by expert intermediary facilitators, in which community members co-design oil sands wetland reclamation and their choices are integrated into life cycle assessments (LCAs) of wetland designs and remediation products. Hybridizing LCA with local co-design not only generates an effective wetland-material choice, but reduces life cycle impacts and increases the likelihood of community acceptance.
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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 | 0.030 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".