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Record W1893714178 · doi:10.29173/pandpr21171

A Review of Brendon Larson’s Metaphors for Environmental Sustainability: Redefining our Relationship with Nature

2013· review· en· W1893714178 on OpenAlexaffvenue
Kevin Redmond

Bibliographic record

VenuePhenomenology & Practice · 2013
Typereview
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsMemorial University of Newfoundland
FundersNational Institutes of Natural Sciences
KeywordsSustainabilityNarrativeAmbiguityValue (mathematics)Environmental ethicsSociologyEpistemologyEcologyPhilosophyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.354
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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