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Record W2738000304 · doi:10.1108/caer-02-2017-0028

Promise, problems and prospects: agri-biotech governance in China, India and Japan

2017· article· en· W2738000304 on OpenAlexaff
Jen Ma, Brad Gilmour, Hugh Dang

Bibliographic record

VenueChina Agricultural Economic Review · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsGlobal Affairs Canada
Fundersnot available
KeywordsCorporate governanceIncentiveScarcityChinaBusinessAgricultureBiotechnologyAgricultural biotechnologyPopulationInternational tradeEconomic growthEconomicsPolitical scienceMarket economyFinanceBiology

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine the potential of agri-biotech to play a role in meeting the world’s food, feed, fiber and fuel needs. Using case studies, policy developments in the key Asian countries of China, India and Japan are also scrutinized to determine the extent to which they enable or obstruct biotech’s potential. Design/methodology/approach The authors first examine some key challenges facing the agriculture and agri-food sector and the potential role biotech can play in addressing them. These challenges include feeding the world’s growing population, improving nutrition worldwide, dealing with allergen risks, reducing nutrient and chemical loading in watersheds, addressing water scarcity issues, and reducing waste in the food system. The authors then turn their attention to the agri-biotech systems in three Asian giants, including China’s centralized governance approach, India’s central-local policy and regulations, and Japan’s pragmatic and evidence-based regulatory framework. Findings Each nation has evolved its own system of governance based on the different challenges facing the society, the recognized potential of different biotech interventions, and citizens’ collective perceptions regarding both the potential and the risks that biotech innovations embody. Systems that are less evidence-based appear to be more discretionary and therefore are less predictable in their outcomes. This increases risks to prospective exporting firms and importing firms, driving up system costs and effectively serving as barriers to entry and to trade. It also dampens and distorts entrepreneurial and innovation incentives. Research limitations/implications From the review and observations the authors then discuss ways and means of establishing priorities through a risk assessment framework in which key risks are enumerated and assessed in terms of their likelihoods and their conceivable consequences. Such an approach would allow challenges to be met with a degree of foresight and adaptability. Practical implications The sometimes disjointed, sometimes strategic use of biotech regulations have fragmented markets and created fiefdoms which undermine the potential of novel technologies to address the challenges facing society. Social implications For illustrative purposes, the authors touch on land and water governance, regulatory and institutional bottlenecks and reforms and the potential for agri-biotech to play an elevated role if vested interests and obstructions can be overcome. Originality/value This study draws on research and literature from several disciplines. It also includes discussions relating to bureaucratic and administrative behavior which erodes the extent to which markets can be contested. This results in balkanized markets and non-cooperative behavior that undermines and distorts incentives for entrepreneurial effort and innovation. That such behavior takes place in markets and disciplines that are fundamental to assuring food security, nutrition and health, as well as good governance of scarce water and land resources is of considerable concern.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.536

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 head0.005
GPT teacher head0.243
Teacher spread0.238 · 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 designObservational
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

Citations7
Published2017
Admission routes1
Has abstractyes

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