Illustrating integrated sustainability and resilience based assessments: a small-scale biodiesel project in Barbados
Bibliographic record
Abstract
Assessments today need to help reverse trends towards deeper unsustainability and address the unavoidable interconnections, feedbacks and uncertainties that typify complex socio-ecological systems at all scales. To illustrate one promising approach, this paper describes a modest effort to integrate understandings from Gibson et al's approach to sustainability assessment with the Resilience Alliance's applications of complex systems thinking into a suite of systems and sustainability based criteria. The integrated sustainability-resilience criteria were used to assess an existing small-scale biodiesel operation on Barbados that involves waste management, public health, transportation, energy security and community involvement considerations. The assessment revealed that the main benefit of this biodiesel project is in social learning rather than enhancing energy security and waste management, and the best ways of enhancing the project lie in larger scale policy initiatives. The findings suggest that the use of a sustainability-resilience approach can contribute insights unlikely to emerge from more narrowly focused assessments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".