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Record W2807809368 · doi:10.3920/jiff2017.0073

The impact of the insect regulatory system on the insect marketing system

2018· article· en· W2807809368 on OpenAlexaffabout
Anu Lahteenmäki‐Uutela, Louise Hénault-Éthier, Siva Barathi Marimuthu, S. Talibov, R.N. Allen, Vivek Nemane, Grant W. Vandenberg, D. Józefiak

Bibliographic record

VenueJournal of Insects as Food and Feed · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Utilization and Effects
Canadian institutionsUniversité LavalKidney Foundation of Canada
Fundersnot available
KeywordsInsectBusinessMarketingEcologyBiology

Abstract

fetched live from OpenAlex

Taking the macromarketing approach to insect food and feed, we study how the global insect marketing system is impacted by the global insect regulatory system. As an illustration, we study how the regulations of the European Union, USA, Canada and Australia impact marketing strategies of individual companies, and how company-level behaviour combines into the dynamics of the whole insect marketing system. The output of the global insect marketing system is the global assortment of insect products. The regulatory system has its topics, content, and tools with differences between countries. Topics are the elements of the insect business that regulators care about. Content determines what insect products can be launched. Tools are the regulatory instruments and sanctions. Regulatory differences between countries are an important determinant in the geography of launch patterns and in the resulting global assortment of insect products available.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.019
GPT teacher head0.220
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2018
Admission routes2
Has abstractyes

Explore more

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