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Record W2966112705 · doi:10.9734/ajeba/2019/v12i130141

Market Making and the Role of Intermediary Firms in Marketing of Genetically Modified Livestock Products

2019· article· en· W2966112705 on OpenAlexaff
Morteza Haghiri

Bibliographic record

VenueAsian Journal of Economics Business and Accounting · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioeconomy and Sustainability Development
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsIntermediaryBusinessProduction (economics)Industrial organizationIntermediationCommercializationAgricultureInvestment (military)LivestockMarketingWork (physics)Process (computing)EconomicsMarket economyMicroeconomics

Abstract

fetched live from OpenAlex

Agricultural biotechnology, by changing the process of agricultural production in the agri-food sector has posed serious challenges for the industry. The fundamental problem was that the biotechnology industry, with tremendous vertical integration from the research sector through to farm gate, has still relied upon decentralized markets to commercialize their products. Their innovations have for the most part been left to find their consumer markets.
 This work was done by an illustrated review of the existing literature and reports.
 The major contribution of this study to the economic literature is three-fold. First, it addresses an important issue in marketing GM livestock products. Second, the study discusses the theory of market microstructure in production economics. Third, it provides policy implications for managers and policymakers in the industry.
 This paper examined the theory of market-making and the role of intermediaries in creating new markets and hypothesizes that without intermediation in the biotechnology market, the optimal market size will not be realized, reducing private research investment and depriving society of the potential social gains of this new technology.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.132

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.171
Teacher spread0.165 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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