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Record W2098815206 · doi:10.1371/journal.pbio.1001344

The Shifting Boundaries of Sustainability Science: Are We Doomed Yet?

2012· article· en· W2098815206 on OpenAlexaboutno aff
John Matthews, Frederick Boltz

Bibliographic record

VenuePLoS Biology · 2012
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilitySustainability scienceFraming (construction)EcologyBiosphereSystems ecologySustainable developmentEnvironmental ethicsSustainability organizationsApplied ecologySociologyBiologyBiodiversityGeography

Abstract

fetched live from OpenAlex

In this issue of PLoS Biology, Burger and colleagues make several important contributions to the discourse of sustainability science, recalling limits of human economic and population growth derived from macroecology and physical principles [1]. We agree with many of the points offered in their paper in this issue and with those in the paper by Brown and colleagues [2]. However, we also believe there is danger in a vision of sustainability that is overly deterministic and does not reflect the dynamic nature of the biosphere, its ecosystems, and economies. We are also concerned about the implications of framing sustainability in the language of physics rather than ecology. Recent policy discussions in preparation for the Rio+20 Convention emphasize the concept of “green economies.” Perhaps most cogently described by microbiologist Lynn Margulis, the term refers to any theory of economics that views human economic activity as embedded within ecosystems. Green economics is often used with or in place of the more widely used term of “sustainability” or “sustainability science.” Both terms reflect a new, evolving, and diffuse discipline—or perhaps a goal approached through many disciplines, including ecology, economics, engineering, and sociology. Given the central role of ecosystems in current paradigms for sustainable development, the science of ecology is a seemingly natural home for sustainability science. However, ecology may also present some operational limits to assessing or implementing sustainable strategies. Given how difficult it is to develop ecological experiments and test hypotheses, ecology has been described as having more in common with the earth sciences (such as geology) than other biological sciences (such as physiology or molecular biology), and much less with physical sciences such as chemistry and physics [3],[4]. Given the importance of observation and inference in ecology, making predictions about complex ecological interactions requires accepting their inherent uncertainty and thus a particular humility in drawing conclusions [5]. A reader of the Burger and colleagues paper [1], for instance, might assume that the logical endpoints for its arguments are either an imminent global economic collapse triggered by stringent natural resource scarcities or catastrophic human population decline in a forceful realignment with global carrying capacity. These are dire options, with no realistically actionable response, and a reader would be forced to either reject the initial assumptions or to despair, neither of which is a useful motivational force for positive change. Moreover, while we believe that heightened concern is warranted and that these endpoints are possible, we also believe there is evidence that they can be avoided or mitigated. Predictions made on similar first principles have been put forward repeatedly in the past (e.g., [6]–[8]), and rigidly materialist approaches to social and economic change often underestimate the flexibility and resilience of human economies and societies [9]. To date, technological advances such as increases in agricultural productivity spurred by the prospect or reality of scarce primary inputs (land, water, nutrients, energy), shifts in economic valuation, and policy-based human behavioral change, such as the actions under the Montreal Protocol to reduce tropospheric concentrations of ozone-depleting gases, have avoided or delayed our transgression of perceived thresholds in the Earth system [10],[11]. While we cannot assume that there is an equivalent to Moore's Law of semiconductor capacity for natural resource management [12] or have faith that efficiency and innovation alone will save us, we can credibly assume that the existential imperative for human adjustment and adaptation will prompt us to correct our seemingly disastrous course. As a result, we believe that sustainability itself must rest on a broader foundation, particularly if we posit that sustainability science encompasses socioeconomic development, which requires the mobilization of natural resources in new ways to sustain and improve human well-being. Here, we describe several potential gaps in sustainability science, as well as evidence for what we hope is useful optimism that emerging economic paradigms are becoming more ecologically sensitive.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.079
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.003
Science and technology studies0.0130.047
Scholarly communication0.0320.072
Open science0.0060.020
Research integrity0.0340.053
Insufficient payload (model declined to judge)0.0230.006

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 head0.028
GPT teacher head0.321
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations21
Published2012
Admission routes1
Has abstractyes

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