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Record W3204993346 · doi:10.17170/kobra-202102163265

Finding alternatives: Canadian attitudes towards novel foods in support of sustainable agriculture

2021· article· en· W3204993346 on OpenAlexaffabout
Janet Music, Jesse Burgess, Sylvain Charlebois

Bibliographic record

VenueKobra (Universitätsbibliothek Kassel) · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAgricultureSustainable agricultureOrder (exchange)BusinessQuarter (Canadian coin)Investment (military)SustainabilityFood systemsNatural resource economicsMarketingFood securityEconomicsGeographyEcologyPolitical scienceBiology

Abstract

fetched live from OpenAlex

Global agriculture and farming practices account for roughly a quarter of total atmospheric emissions. Protein agricultural is especially prone to green-house gas emissions. There is a need to find alternatives, both in the form of protein and sustainable practices in providing alternative protein sources. However, sustainable agricultural practices must consider consumer behaviour and attitude towards switching protein sources. In this study, we carried out a survey of 993 Canadians in order to better understand the likelihood of adoption of alternative proteins, cultured meat, insects and jellyfish; attitudes towards sustainable agriculture were also explored. Results show that novel foods that imitate traditional protein sources have a higher acceptance rate than those that are not part of the cultural food landscape. There is no evidence that consumers would switch from traditional protein sources when given more protein source options, calling into question the environmental efficacy of novel food offering. This suggests that investment in alternative proteins as sustainable agriculture requires consumer engagement in order to see widespread success.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.225
Teacher spread0.210 · 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 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
Published2021
Admission routes2
Has abstractyes

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