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Record W2802494255

DECONSTRUCTING PUBLIC PERCEPTIONS OF NOVEL FOOD TECHNOLOGIES: HUMAN VALUES AND INFORMATION COMMUNICATION STRATEGIES

2018· dissertation· en· W2802494255 on OpenAlexfundaboutno aff
Yang Yang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsnot available
FundersMinistry of Agriculture - Saskatchewan
KeywordsPerceptionData scienceInformation and Communications TechnologyComputer sciencePsychologyWorld Wide WebNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

Based on insights of behavioural economics, this thesis aims to provide a more nuanced understanding on determinants of consumers’ acceptance of novel food technologies. In particular, this thesis explores how consumers’ attitudes and food choices related to innovative food technologies are affected by ‘inside’ individual factors, such as underlying human values (i.e., cultural worldviews and food-related values), and ‘outside’ environmental factors, such as the information framing (i.e., narrative communication). Each paper focuses on one particular factor that motivates disparate assessments of food technologies.\n\nEmpirical data on consumers’ food technology attitudes and food choice behaviours were collected from a nation-wide Internet survey administered to 1608 Canadian consumers in 2016. Half of the respondents participated in the Biotechnology version survey, and the other half of respondents completed the Nanotechnology version survey. Both versions of online survey incorporated a choice experiment, where respondents selected their most preferred sliced apple products from a set of hypothetical alternatives. Each paper focuses on particular research questions thus uses different sections of this extensive survey.\n\nPaper 1 explores information framing effects by comparing the effectiveness of using logical-scientific vs. narrative information to communicate about food biotechnology to consumers. A logical-scientific information condition about biotechnology was developed and written in a scientific style using the passive voice with generalized and impersonal language. In contrast, a narrative-style information condition about the technology was written in a more lively and vivid personal style. Respondents were randomly assigned to different information treatments. Results indicate that information about food biotechnology shown in different formats (logical-scientific vs. narrative) or being accessed by respondents in different manners (forced exposure or voluntary choice) can have differing impacts on perceptions and preferences. Compared with logical-scientific information, narratives and/or voluntary information access could help to reduce the opposition to biotechnology.\n\nPaper 2 investigates an alternative psychosocial factor, cultural worldview, which has been underestimated or omitted when examining consumer acceptance of food biotechnology. Individuals’ cultural worldviews were measured by a slightly modified version of cultural cognition scale. Results suggest that individuals holding hierarchical (vs. egalitarian) and communitarian (vs. individualistic) worldviews tend to hold more positive attitudes and be more accepting of agricultural biotechnology.\n\nPaper 3 suggests that intermediary food-related values and their relative importance to consumers have significant powers in explaining attitudes and choices about foods produced by means of nanotechnology. Consumers are heterogeneous in their food values, i.e., they place different importance on food value items such as naturalness, appearance, convenience, safety and novelty. Although Canadian consumers, on average, prefer not to use nanotechnology in sliced apple production, their preferences are heterogeneous. ‘Supporters’ of nanotechnology applied to agriculture and food production are those who consider ‘appearance’ is an important value to food purchase. By contrast, ‘opponents’ tend to emphasize the importance of ‘naturalness’ and ‘origin’.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.010
Open science0.0000.000
Research integrity0.0010.000
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 head0.034
GPT teacher head0.258
Teacher spread0.224 · 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 designTheoretical or conceptual
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

Citations3
Published2018
Admission routes2
Has abstractyes

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