A Review of Brendon Larson’s Metaphors for Environmental Sustainability: Redefining our Relationship with Nature
Bibliographic record
Abstract
Brendon Larson’s Metaphors for Environmental Sustainability: Redefining our Relationship with Nature is a thought provoking treatment of what can be a challenging and sometimes controversial subject. Primarily, but not exclusively, through four feedback metaphors: progress, competition, barcoding, and meltdown, Larson challenges the dominant scientific discourse, highlighting the limits of a single-lens scientific narrative while emphasizing the value of welcoming ambiguity, and diversity as a means to fruitful discussion and inquiry in addressing the issues surrounding environmental sustainability. Furthermore, rather than fencing ourselves off from nature, Larson demonstrates the importance of breaking down narratives of duality, and seeing ourselves as one with nature, not separate from it, in addressing issues concerning environmental sustainability. This book is valuable not only for its message, but also for how its concepts are presented. Larson presents historical and cultural frameworks to contextualize evolutionary and current environmental sustainability narratives. This book exemplifies phenomenological practices and perceptions, and is a valuable and insightful read for any individual, practitioner, or academic with an interest in environmental sustainability.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".