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Record W2770369428 · doi:10.1111/conl.12428

Shining a light on seafood mislabeling through respectful debate

2017· article· en· W2770369428 on OpenAlexaff
Megan Bailey

Bibliographic record

VenueConservation Letters · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBusinessInternet privacyNatural resource economicsPsychologyEconomicsComputer science

Abstract

fetched live from OpenAlex

As I write this, the U.S. National Academy of Science is being sued by Dr. Mark Jacobson, a professor of civil and environmental engineering at Stanford University. Dr. Jacobson alleges that publication of a rebuttal, which he claims to be inaccurate and false, to a contribution of his in the Proceedings of the National Academy of Sciences has had grave ramifications for his career (Woolston, 2017). The potential precedent that the outcome of this case has on science and scientific debate cannot be overstated. Science is the pursuit of knowledge, a pursuit which is additive and only through debate, dialogue, and discomfort do we enter into new frontiers of understanding. So, debate is necessary, but what is also necessary is for that debate to take place in a way that is respectful and constructive, particularly given we are at a time when the relevance of science in the quest for truth is being questioned (Higgins, 2016). This balance of respectful criticism plays out all the time in the scientific literature. One such example is the publication of, and three subsequent rebuttals to, Stawitz et al.’s 2016 article on the “Financial and ecological implications of global seafood mislabeling” (Stawitz, Siple, Munsch, Lee, & Derby, 2017). The intention in this viewpoint is not to weigh in on the merits of the original article, or those of the rebuttals, but rather to provide a summary of the debate, and why it is important for such debate to take place as we work toward seafood sustainability. Seafood mislabeling occurs when a product labeled as one species is in fact actually a different species, and can be the result of intentional product substitution, or can occur by accident. The complexity of seafood supply chains means that seafood products often go through multiple instances of product transformation and change hands internationally, introducing several different avenues for mislabeling to accidently occur. But often, a lower cost fish or seafood product is intentionally labeled as one that is more expensive (and often rarer), for example, escolar is often sold as “white tuna,” pink salmon can be sold as “sockeye,” and tilapia can be sold as “snapper.” Mislabeling can have consequences for human health, when the substitution involves a species that may pose harm, for consumers’ wallets as they may be paying more than the market would otherwise charge for that product, and for ecosystem health, as we often look to market signals like supply as evidence of healthy fish (i.e., if tuna and sockeye and snapper are for sale, they must be plentiful in the environment) (Carvalho, Neto, Brasil, & Oliveira, 2011; Helyar et al., 2014; Jacquet & Pauly, 2008; Miller & Mariani, 2010). In their original contribution, Stawitz et al. provide a meta-analysis of previous studies that use DNA barcoding to examine rates and types of seafood mislabeling. They compare these studies with the price and conservation status of the product advertised on the label, and that of the actual substituted product. Of particular relevance to the credibility of their conclusions is their use of the International Union for Conservation of Nature, IUCN, for data pertaining to conservation status. They conclude that because the substituted products were sold for less than the advertised product, and that the substituted product was generally of a better conservation status, the financial and ecological implications of mislabeling are more likely to be positive than negative. The three rebuttals focused on limitations of the methods, sweeping generalizations that were not supported by the data, and on the management implications of the study. Mariani et al. (Mariani, Cawthorn, & Hanner, 2017) and Donlan et al. (Donlan et al., 2017) highlight the use of IUCN conservation status as an indicator of sustainability. Their concerns highlight the fact that there are several species that have not even been assessed by the IUCN, and several replacement species that are farmed and not wild-caught, meaning the IUCN status does not apply. For example, when Atlantic salmon (Salmo salar) was used as a substitute for a wild-caught Pacific salmon species or rainbow trout, the former was listed as being more of a conservation issue. But the substitute is likely farmed and not wild-caught Atlantic salmon, meaning conservation status is a moot point. Warner et al. (Warner, Miller, & Naaum, 2017) also focus on the use of the IUCN conservation status, but do so because the authors use IUCN averages, which may miss major differences of species within one genus. Warner et al. also argue that the assertion in the original article that mislabeling should be tackled through chain of custody improvements puts the responsibility on downstream private actors, for example, retailers, instead of acknowledging that upstream public actors, for example, Port States, also have management responsibility in tackling seafood mislabeling. The original authors responded to the three rebuttals (Siple, Stawitz, Munsch, & Lee, 2017), emphasizing that their intention was not to suggest that mislabeling has conservation benefits, but rather that the implications of mislabeling should not be generalized as necessarily being bad (or good), and that the variety of causes and impacts of mislabeling should be considered in making management interventions. The rebuttals also forced the authors to not only clarify their conclusions and arguments, but also to reanalyze their data, and in doing so, they found some errors (Siple et al., 2017). The original contribution calls for increasing seafood traceability and value chain transparency as potential solutions to address seafood mislabeling. One might think that regardless of the impacts of seafood mislabeling, better supply chain transparency is probably something all researchers would agree on. Yet, there is also a growing body of literature studying the efficacy and effectiveness of seafood transparency and traceability in supporting just and sustainable fisheries (Bailey, Bush, Miller, & Kochen, 2016; Bailey & Egels-Zandén, 2016; Hardt, Flett, & Howell, 2017). So, through this study, and the respectful debate thereof, we improve not only the study itself, but we also enter into a new debate about management and governance interventions. One of the biggest challenges I have found working with graduate students is their desire for perfection before publication. While we need to focus on doing good science, we also need that science out in the community in order for respectful debate to improve on our methods, to challenge our assumptions, and to strengthen our understanding of the world around us. “Don't let perfect be the enemy of good” is a common phrase I say to my students. But good has to be good enough to ensure the credibility of science, and ensure that respectful debate can continue in a way that benefits both science and citizenry.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.519

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 studies0.0010.000
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 head0.045
GPT teacher head0.305
Teacher spread0.259 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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