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Record W3080565440 · doi:10.1111/conl.12751

A green new deal for the oceans must prioritize social justice beyond infrastructure

2020· article· en· W3080565440 on OpenAlexaff
Andrés M. Cisneros‐Montemayor, Katherine M. Crosman, Yoshitaka Ota

Bibliographic record

VenueConservation Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEquity (law)VisionSustainabilityClimate changeIndigenousEnvironmental justiceClimate justicePolitical scienceBusinessEconomic growthEnvironmental resource managementEnvironmental planningGeographySociologyEconomicsEcology

Abstract

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In a recent and very timely contribution, Dundas et al. (2020) highlight the importance of extending the values and proposed strategies of the Green New Deal (GND) proposed in the U.S. Congress (https://www.congress.gov/bill/116th-congress/house-resolution/109/text) to the oceans. Dundas et al. (2020) convincingly argue that investing in infrastructure, renewable energy, food security, and habitat restoration is essential for ocean spaces (which are inextricably linked to terrestrial systems) experiencing rapid climate change. Ocean development is furthermore at a crossroads, with emerging visions of economic expansion that must integrate environmental sustainability and social equity concerns (Cisneros-Montemayor, Moreno-Báez, et al., 2019). We agree that investments proposed under the GND are needed to enable future sustainable and equitable development by acknowledging climate change, anticipating future challenges, and proactively transforming the U.S. economy (Dundas et al., 2020). However, one important theme of the GND that received little emphasis in Dundas et al. (2020) is that of justice and equity in the oceans. The GND is specifically intended to promote social justice and to address historical and continuing inequities experienced by “frontline and vulnerable communities” while mitigating and adapting to the effects of climate change. The vast majority of ocean users around the world indeed form part of such communities, including artisanal fisherfolk who constitute 90% of employment in ocean sectors (Cisneros-Montemayor, Moreno-Báez, et al., 2019), and the 27 million coastal Indigenous peoples across the world's coastlines and seas (Cisneros-Montemayor, Pauly, Weatherdon, & Ota, 2016). Thus, capitalizing on investment in a way that realizes the vision of the GND requires us to consider and stress the importance of “ocean equity” during discussions on necessary industrial transitions. The principles of the GND are globally relevant and highly pertinent for addressing equity and justice in oceans. The world's oceans are affected by complex economic and cultural connections; governing them sustainably requires careful policy and planning. Solutions must recognize complex political dynamics and focus on the needs and preferences of the less powerful, rather than relying on processes that allow the powerful to claim progress while shifting costs to those with less opportunity to meaningfully object. In the United States and beyond, national and multilateral ocean governance can make a difference by applying the guidelines of the GND and prioritizing the livelihoods, experiences, and voices of frontline ocean communities. This entails addressing historical and current inequities both between and within nations and sectors (Bennett, Blythe, Cisneros-Montemayor, Singh, & Sumaila, 2019), recognizing diversity in human and natural ocean systems (Cisneros-Montemayor, Cheung & Ota, 2019), and reconciling multiple development goals (Singh et al., 2018). Mitigating and adapting to climate change requires deep transformations of our industrial and economic systems, but directly addressing issues of social justice and equity is key for advancing sustainability and well-being in coastal communities and beyond. All authors contributed equally to the conception and writing of the manuscript. The authors declare no conflict of interest. There are no data related to this manuscript and therefore not ethics review process was applicable. There are no data related to this manuscript.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.305
Threshold uncertainty score0.497

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 studies0.0000.000
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 head0.016
GPT teacher head0.218
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations3
Published2020
Admission routes1
Has abstractyes

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