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Record W2977667889 · doi:10.1007/s10784-019-09452-9

Understanding the limitations of current RFMO climate change adaptation strategies: the case of the IATTC and the Eastern Pacific Ocean

2019· article· en· W2977667889 on OpenAlexaff
Brian Pentz, Nicole Klenk

Bibliographic record

VenueInternational Environmental Agreements Politics Law and Economics · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIncrementalismClimate changeEnvironmental resource managementAdaptation (eye)Resource (disambiguation)Ecological forecastingSustainable developmentAdaptive managementBusinessEnvironmental planningPaceNatural resource economicsPolitical economy of climate changeGeographyEnvironmental sciencePolitical scienceEcologyEconomicsComputer science

Abstract

fetched live from OpenAlex

While Regional Fisheries Management Organizations (RFMOs) face many challenges in their pursuit of sustainable resource development, climate change is among the most pressing and least addressed. Research has identified a host of expected or ongoing physical, biological, ecological, and social impacts of climate change on the marine environment, creating a strong climate change adaptation imperative for RFMOs. Through a case study of the Inter-American Tropical Tuna Commission (IATTC), we describe two serious limitations of current RFMO climate change adaptation strategies: (1) a weakened efficacy of resource management and conservation policies caused by viewing climate change as a general climate stressor rather than a unique environmental challenge, and (2) a reliance on incremental policy reform, problematic because it may not enable a pace or scale of policy change proportional to the sustainable development challenges created by a rapidly changing ocean. We discuss the benefits and drawbacks of incrementalism and outline potential solutions to the environmental and structural challenges facing the IATTC and other RFMOs, including the concept of adaptation pathways.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.090
GPT teacher head0.232
Teacher spread0.142 · 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 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

Citations13
Published2019
Admission routes1
Has abstractyes

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