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Record W6987453684

Sustainability as Justice Engaging With North American Alternative Seafood Networks Through Participatory Action Research

2022· article· en· W6987453684 on OpenAlexaboutno aff

Bibliographic record

VenueDigitalCommons (California Polytechnic State University) · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsParticipatory action researchFood securityEnvironmental justiceEconomic JusticeSustainabilityFood systemsCitizen journalismOverfishingAction (physics)
DOInot available

Abstract

fetched live from OpenAlex

Transformations in the ways we relate to the ocean are long overdue given the myriad of anthropogenic problems that exist – from overfishing to plastic pollution and acidification to ‘slavery-at-sea ’ and loss of access and fishing rights. Yet alongside the hegemonic modes of ocean exploitation exist diverse alternative economies, including those associated with alternative seafood networks, that aim to create different and more-than-economic relationships with marine systems. To situate my research within the broader literature, I interpret the widely used Brundtland Report definition of sustainability, “meet[ing] the needs of the present without compromising the ability of future generations to meet their own needs” (WCED 1987, p. 43), as intra- and inter-generational justice, in line wit h Gottschlich and Bellina (2016), Fredericks (2012), and Baumgartner and Quaas (2010). My thesis begins in Chapter 1with an examination of food security through a sustainability-as-justice lens, incorporating an analysis of seafood production compared to undernourishment by country, a literature review of the transformative potential of alternative seafood networks, and policy and market-based recommendations for U.S. practitioners. For Blue Transformation, the Food and Agriculture Organization of the United Nation’s plan foraquatic food systems from 2022-2030 (FAO 222), t o meaningfully contribute to food security as intended, a sustainability-as-justice lens is necessary to ensure procedural, distributive, and recognitive forms of justice important to the pillars of food security. This justice lens ultimately calls into question afundamental normative assumption of sustainable development –economic growth. In Chapter 2, I present results from participatory action researchas a participatory and emancipatorymethod–a way of enacting sustainability-as-justice. Questioning the extent of justice enacted through existing seafood sustainability certifications and motivated by the desire of seafood enterprise operators to hold themselves accountable and apart from a seafood system they seek to transform, this research lays the groundwork for an alternative to existing third-party seafood certifications. This research was inspired by Participatory Guarantee Systems, a peer-reviewed alternative to Organic certification for small-scale and alternative agricultural producers. Through this collaborative project, I worked with members of the Local Catch Network, a community-of-practice made up of alternative seafood networks from across the United States and Canada. Together, wecreateda self-evaluationtool to help seafood enterprise operatorsevaluate their practices in relationship to the Local Catch Network’snine core values, which encompass social, economic, and environmental sustainability. The output from this tool includes a set of103 accountability indicators that encompass multiple facets of justice and have the potential to form the basis for a seafood specific Participatory Guarantee System. By incorporating indicators both related to individual business practices and levels of collaboration in advocacy, this research sets up a system for future analysis of the capability of alternative seafood networks to both self-transform as well as to create change in the wider seafood system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.006
Research integrity0.0000.001
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 head0.042
GPT teacher head0.282
Teacher spread0.240 · 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
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

Citations0
Published2022
Admission routes1
Has abstractyes

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