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Record W4416668786 · doi:10.3389/fgeed.2025.1740380

Editorial: Social aspects of crop genome editing

2025· editorial· en· W4416668786 on OpenAlexaffabout
Srividhya Venkataraman, Kathleen Hefferon

Bibliographic record

VenueFrontiers in Genome Editing · 2025
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultinational corporationPublic domainIntellectual propertyDiscernmentPoliticsGenome editingDeveloping countryPromotion (chess)

Abstract

fetched live from OpenAlex

The price of research and development for generating genome-edited crops as well as their regulatory approval can exacerbate disparities within and between nations. Further, there prevail economic concerns regarding a minority of big, multinational corporations acquiring intellectual property rights and controlling the seed market. Patented seeds can incur financial encumbrance for small farmers, particularly in developing countries. Also, there is dispute regarding whether genome-edited crop products must be labeled. Some groups call for mandatory labeling to protect consumers rights to information and opting for these products while on the other hand, other groups express anxiety that labeling could lead to rejection by the public, thus hindering innovation. Varying regulatory policies across different countries engender major challenges to international trade and promotion of new technologies. For instance, in Europe, process-based perspective on GE crops contrasts with the predominantly product-based approach adopted in Brazil, Argentina and the USA. Such differing political choices and not scientific disparities are the principal drivers of disparate regulatory frameworks.Many of the world's populace lack adequate awareness of genome editing resulting in varying and complex public perceptions. A lack of discernment can lead to mistrust in genome-edited crops with some people associating these crops with the controversial GMOs. As a result, consumers display varied attitudes towards crop genome editing ranging from approval of valuable genome-edited foods such as in Japan to profound safety concerns. Some public associations and non-governmental organizations (NGOs) actively advocate against gene-edited crops, magnifying public misconceptions and influencing policies. Public approval or rejection pose important stumbling blocks to commercialization. Poor acceptance from food consumers and retailers can endanger the economic credibility of novel crop varieties irrespective of their benefits. Also, the various stumbling blocks regarding the use of genome editing technology including long-term outcomes, off-target effects and other associated issues are addressed. The major content of this review covers the current regulatory scenario, opportunities as well as challenges towards the adoption of genome edited crops for use by the population spanning countries such as North America and Europe in addition to the differing levels of consumer acceptance in these countries. Discrepancies pertaining to the regulatory approval schemes in several countries of the Northern part of the world will profoundly affect those of the Southern regions who have the most to benefit from these burgeoning novel technologies towards enhanced agricultural outcomes.In the article by Sato, entitled "Genome Editing and Genetic Modification in the Humanities and Social Sciences: A Bibliometric Comparison Using Web of Science," 2,962 academic articles published in the Web of Science database between 1950 and 2024 were analyzed and revealed that most of the genetic modification research pertains to agriculture, the environment, and policy, whereas genome editing research focused more on bioethics and medical applications. Furthermore, the author found that genetic modification topics focused on regulation, whereas genome editing focused on consumer acceptance, suggesting that each technology requires an independent form of dissemination to the general public.Similarly, "The decision to purchase genome edited food products by Iranian consumers: Theory of Planned Behavior as a social intervention tool" by Valizadeh and Karami, explored consumer attitude towards gene-edited food products. The authors found that public trust in gene-edited products and the perceived benefits of gene-edited products had positive and significant effects on the consumer behaviour and intention to purchase these products. The authors conclude that their study could provide a framework for future interventions that would improve consumer preference for food products that are genome edited.Vasquez et al., paper entitled "Canadian Consumer Preferences Regarding Gene-Edited Food Products" explores consumer transparency and limitations in information regarding genome edited food products. The authors found four main factors which strongly influence consumer perceptions: trust in the Canadian food safety system; food technology neophobia scores; knowledge of genetics; and self-knowledge of gene editing.Survey participants indicated that nutrition, price, and taste were the three most important values. The strongest contributing factor affecting willingness to consume is the environmental impact of the production of genome edited crops. Canadian consumers largely experience more trust in genome edited rather than genetically modified crop food technologies.Tachikawa and Matsuo's paper, "New Assemblages and Governance Issues Associated with Genome-Edited Crops" how genome editing technology is reshaping relationships between crops and neighboring wild species, as well as other organisms, is addressed.How these relationships can be parsed out as governance issues is addressed.In summary, the studies presented in this Research Topic enhance our understanding of how society is currently responding to the topic of genome editing in our food system.It will be intriguing to track how things unfold from here.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
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 head0.003
GPT teacher head0.258
Teacher spread0.255 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2025
Admission routes2
Has abstractyes

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