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Record W3151388212 · doi:10.3389/fevo.2021.648550

Equity – the Bottleneck and the Opportunity

2021· article· en· W3151388212 on OpenAlexaff
Jake Rice

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsEquity (law)SustainabilityPovertyDisadvantagedPublic economicsContext (archaeology)EconomicsSustainable developmentEnvironmental resource managementBusinessDevelopment economicsPolitical scienceEconomic growthEcologyGeography

Abstract

fetched live from OpenAlex

There is a widespread tendency for diverse uses of Nature, on scales from small and local to very large, to become unsustainable. Once unsustainable, bringing a use back to sustainability and keeping it sustainable then takes substantial effort and tools appropriate to the context of the use. This Perspective first asks why is the tendency for unsustainability so pervasive, when it is an outcome that no user group has adopted as an objective, and ways to keep uses sustainable are known. I argue and present evidence that the common factor underlying the pervasiveness of unsustainable uses of Nature is inequity in the distribution of the benefits created from those uses, with both the wealthy “winners” of the distributional inequities and those disadvantaged and in poverty driving uses toward increasing unsustainability in ways that depend on the nature of the inequities. Unless the inequity of distribution of benefits from uses of Nature is addressed as an issue in its own right, there are few or no pathways to medium or long-term sustainable use. However, if inequity is addressed broadly and effectively, many pathways are available and societies can select the pathways appropriate to their cultural and ecological contexts.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.215
Teacher spread0.160 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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