UNDERSTANDING CONSUMER ACCEPTANCE OF GENETICALLY MODIFIED FOODS IN CANADA: AN EXPLORATION OF THE INFLUENCE OF CULTURE ON CONSUMER PLANNED BEHAVIORS

Genetically modified (GM) food is playing an increasingly important role in the global food supply chain but is still a controversial topic with consumers. This study aims to better understand consumer acceptance of GM foods and the influences of culture in Canada. More specifically, this paper investigates antecedents to consumer attitudes with respect to GM foods and how individualism and uncertainty avoidance might moderate the relationships between perceptions of risks and benefits, subjective norms, and purchase intentions. The theoretical framework of this study is based on the Theory of Planned Behavior and Hofstede’s cultural dimensions theory. Specifically, attitude, subjective norm, and perceived behavioral control are proposed as three significant predictors of consumers’ purchase intention of GM foods. In addition, perceived personal benefits are hypothesized to have a stronger influence on attitude among consumers with a more individualist culture compared to consumers with a more collectivistic culture. In contrast, subjective norm is predicted to have stronger influence on purchase intention among consumers with more collectivistic culture. Moreover, perceived risks are hypothesized to have a stronger influence on attitude among consumers with higher scores on uncertainty avoidance. This study employed a questionnaire-based consumer survey to collect quantitative information. The results indicate that consumer attitudes are influenced by perceived personal, social, and industry benefits, and risks. Further, consumers with high uncertainty avoidance place heavier emphasis on the risk factors. The integrated framework and findings of this study provide useful knowledge for both researchers and food marketers to better understand the influence of cultural values in shaping consumers’ attitude and purchase intention. The results have potential implications for Canadian food and agricultural companies with respect to creating more effective strategies to communicate with consumers from diverse cultural backgrounds

CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models

Holistically measuring societal biases of large language models is crucial for detecting and reducing ethical risks in highly capable AI models. In this work, we present a Chinese Bias Benchmark dataset that consists of over 100K questions jointly constructed by human experts and generative language models, covering stereotypes and societal biases in 14 social dimensions related to Chinese culture and values. The curation process contains 4 essential steps: bias identification via extensive literature review, ambiguous context generation, AI-assisted disambiguous context generation, snd manual review \& recomposition. The testing instances in the dataset are automatically derived from 3K+ high-quality templates manually authored with stringent quality control. The dataset exhibits wide coverage and high diversity. Extensive experiments demonstrate the effectiveness of the dataset in detecting model bias, with all 10 publicly available Chinese large language models exhibiting strong bias in certain categories. Additionally, we observe from our experiments that fine-tuned models could, to a certain extent, heed instructions and avoid generating outputs that are morally harmful in some types, in the way of "moral self-correction". Our dataset and results are publicly available at \href{https://github.com/YFHuangxxxx/CBBQ}{https://github.com/YFHuangxxxx/CBBQ}, offering debiasing research opportunities to a widened community

The spread of generalized reciprocal distance matrix

The generalized reciprocal distance matrix $RD_{\alpha}(G)$ was defined as $RD_{\alpha}(G)=\alpha RT(G)+(1-\alpha)RD(G),\quad 0\leq \alpha \leq 1.$ Let $\lambda_{1}(RD_{\alpha}(G))\geq \lambda_{2}(RD_{\alpha}(G))\geq \cdots \geq \lambda_{n}(RD_{\alpha}(G))$ be the eigenvalues of $RD_{\alpha}$ matrix of graphs $G$. Then the $RD_{\alpha}$-spread of graph $G$ can be defined as $S_{RD_{\alpha}}(G)=\lambda_{1}(RD_{\alpha}(G))-\lambda_{n}(RD_{\alpha}(G))$. In this paper, we first obtain some sharp lower and upper bounds for the $RD_{\alpha}$-spread of graphs. Then we determine the lower bounds for the $RD_{\alpha}$-spread of bipartite graphs and graphs with given clique number. At last, we give the $RD_{\alpha}$-spread of double star graphs. Our results generalize the related results of the reciprocal distance matrix and reciprocal distance signless Laplacian matrix.Comment: 14 page

Clustering of Gene Expression Data Based on Shape Similarity

A method for gene clustering from expression profiles using shape information is presented. The conventional clustering approaches such as K-means assume that genes with similar functions have similar expression levels and hence allocate genes with similar expression levels into the same cluster. However, genes with similar function often exhibit similarity in signal shape even though the expression magnitude can be far apart. Therefore, this investigation studies clustering according to signal shape similarity. This shape information is captured in the form of normalized and time-scaled forward first differences, which then are subject to a variational Bayes clustering plus a non-Bayesian (Silhouette) cluster statistic. The statistic shows an improved ability to identify the correct number of clusters and assign the components of cluster. Based on initial results for both generated test data and Escherichia coli microarray expression data and initial validation of the Escherichia coli results, it is shown that the method has promise in being able to better cluster time-series microarray data according to shape similarity