• Safety and Health at Work
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• 제13권1호
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• pp.23-31
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• 2022

Background: Potato is the main crop of Ardabil Plain (accounting for one-fifth of potato production in Iran). Its health hazard risk to farmers is rising due to the increasing rate of pesticide use. The present study analyzes potato farmers' health hazard risk in the use of chemical pesticides. Methods: The rate of pesticide use by farmers (n = 370) was first compared with the recommended dosage (on pesticide label). Then, a composite index was employed to estimate the health hazard risk of farmers during pesticide use, and the variables accounting for pesticide overuse and nonoveruse were analyzed. Safety behavior was examined in four steps, namely of pesticide purchase and storage, preparation, application, and postapplication. Results: It was found that 74.6 percent of potato farmers used pesticides in higher concentrations than the recommended dosage. The higher average rate of pesticide use versus recommendation (label instruction) was related to Chlorpyrifos and Trifluralin, and the highest average health hazard risk among farmers was related to the use of Chlorpyrifos and Metribuzin. Farmers with a higher risk of health hazard displayed much lower safety behavior than the other farmers at all steps of pesticide use. Conclusion: The most important variables discriminating the health hazard risk of farmers' overuse included health behavior identity, attitude, knowledge and awareness, and cues to action. Therefore, using social media, holding local exhibitions, and engaging local leaders and skilled farmers in the region to improve farmers' attitudes and health behavior identity toward the dangers of chemical pesticides can play a significant role in motivating farmers' display of overuse preventive behaviors.

• 인문언어
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• 제6권
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• pp.109-145
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• 2004

Social scientists in North America, especially anthropologists, folklorists and linguists, who focus on the study language use and its connection to society, use a variety of labels to describe what they do. Among the best known are 'anthropological linguistics' , 'linguistic anthropology', and 'sociolinguistics'. All of these labels imply that their focus is on the study of language usage in society and culture for their teaching, research and publications. In this paper I am examining the intellectual issues and history that underlie the differences in the labels. The differences and similarities that characterize them are discussed. The author proposes 'linguistic anthropology' as the most useful disciplinary terminology if the study of language combined with culture is to be 'community-centric' and not only 'profession-centric' . He encourages a renewed focus on working with communities. Also, a need to find ways to engage Indigenous members of minority language communities more actively should be a primary goal in the process of 'academic' language work. This is important due to the loss rapid extinction of the many of the world's languages. The author points out that it does matter what we call the work we do, as a label may carry a message of meaning, intent and focus.

• 전기학회논문지
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• 제67권9호
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• pp.1224-1231
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• 2018

Automatic age estimation has been used in many social network applications, practical commercial applications, and human-computer interaction visual-surveillance biometrics. However, it has rarely been explored. In this paper, we propose an automatic age estimation system, which includes face detection and convolutional deep learning based on an inception module. The latter is a 22-layer-deep network that serves as the particular category of the inception design. To evaluate the proposed approach, we use 4,000 images of eight different age groups from the Adience age dataset. k-fold cross-validation (k = 5) is applied. A comparison of the performance of the proposed work and recent related methods is presented. The results show that the proposed method significantly outperforms existing methods in terms of the exact accuracy and off-by-one accuracy. The off-by-one accuracy is when the result is off by one adjacent age label to the above or below. For the exact accuracy, the age label of "60+" is classified with the highest accuracy of 76%.

• 융합경영연구
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• 제8권4호
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• pp.13-25
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• 2020

Purpose - This study is aiming to provide policy makers with new insights to promote the consumption of locally-grown produces in the HMR industry by exploring what kinds of policies Japan, UK and Italy have developed. Research design - After introduction, the research starts to review the existing literature related to the promotion of local produce consumption, and then, compares the policies introduced by the above countries. Finally, the authors draw conclusions on the basis of research findings. Results - Firstly, central government has to collaborate with local authorities to promote local food consumption in the HMR industry. Secondly, countries have strengthened food label system to enhance local food consumption, in terms of country of origin. Thirdly, all of nations has highlighted food safety to protect customers. Fourthly, the government has created the business environment forcing HMR operators and retailers to follow the government's policy. Fifthly, it is necessary to support the social communities to enhance their social responsibility, from the government's point of view. Lastly, the social responsibility and the ethical administration of retailers should be sustainably strengthened by social atmosphere. Conclusions - In line with the growth of HMR products, governments have to make a considerable effort to develop innovative methods to promote local food consumption in the HMR industry.

• 한국심리학회지 : 문화 및 사회문제
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• 제24권1호
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• pp.29-47
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• 2018

사회적 상황은 개인의 정보처리와 정서경험에 영향을 주는가? 본 논문에서는 두 개의 연구를 통해 사회적 정보처리와 자기참조효과 및 정서경험의 관계를 검증하였다. 연구 1에서는 외부의 명시적 지시없이도 자기개념이 자동적으로 활성화되어, 도형과제를 통해 자신과 연관된 자극의 처리가 친구/타인과 연관된 자극의 처리보다 더 수월한지를 검증했다. 그 결과 자신을 표상하는 자극의 처리가 친구/타인에 대한 자극처리보다 더 촉진되는 경향이 나타났다. 연구 2에서는 참가자들에게 다양한 단어를 보여주고, 자신이 선택한 단어 또는 친구가 선택한 단어라는 설명과 함께 제시된 단어에 대한 기억을 비교하였다. 그 결과 참가자들은 혼자 과제를 수행하는 비사회적 조건에서 친구와 함께 과제를 수행하는 사회적 조건보다 자신이 선택한 단어를 더 많이 기억하는 경향이 나타났다. 이에 비해 사회적 조건에서는 참가자들이 친구가 선택한 단어를 자신이 선택한 단어보다 더 많이 기억하였다. 또한 사회적 조건에서는 실험상황에서 초콜릿 경험에 대해 보고한 긍정적 정서의 강도가 비사회적 조건보다 더 높게 나타났다. 이 결과는 사회적 정보처리가 자동적 자기참조효과를 감소시키며, 타인과의 경험공유는 정서경험을 증폭시킬 가능성을 시사한다.

• 패션비즈니스
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• 제24권2호
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• pp.68-84
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• 2020

The purpose of this thesis is to suggest a Practical Plan for ethical consumption by reviewing the category of Vegan Fashion and investigating its Social Value of vegan fashion. This will be achieved through investigating the papers and official home pages of 13 selected Vegan Fashion brands. It was found that in terms of use of materials such as leather, fur and organic fibers the brands can be divided into three sections: fur-free, cruelty-free and perfect vegan. A Practical Plan is suggested based on the aspects of production, consumption, distribution and education. Firstly, the provider should be required to understand vegan materials deeply, it is also desirable for them to get vegan certifications. Secondly, the seller should also understand about vegan materials, and be able to explain this to consumers. The education from the seller is vital and the meaning of logos and associated contents used by the label should be clearly explained to consumers. Thirdly, the association of consumers, and fashion brands should cooperate to enhance the level of general understanding in society further, this should influence new laws, that address ethical issues regarding the use of fur in fashion. Environmental problem of the future might be reduced if the stakeholders in Vegan Fashion are cooperatively and actively trying to educate the general population and make Vegan Fashion popular and ethical consumption popular.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제12권4호
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• pp.159-172
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• 2023

일반적으로 사회문제 해결 연구는 과학기술을 활용하여 다양한 사회적 현안들에 의미있는 해결 방안을 제시함으로써 중요한 사회적 가치를 창출하는 것을 연구 목표로 한다. 그러나 사회문제와 쟁점을 완화하기 위하여 많은 연구들이 국가적으로 수행되었음에도 불구하고 여전히 많은 사회문제가 남아 있는 상황이다. 사회문제 해결 연구의 전 과정을 원활하게 하고 그 효과를 극대화하기 위해서는 사회적으로 시급한 현안들에 대한 문제를 명확하게 파악하는 것이 중요하다. 사회문제 해결과 관련된 기존 R&D 보고서와 같은 자료에서 중요한 사안을 자동으로 식별할 수 있다면 사회문제 파악 단계가 크게 개선될 수 있다. 따라서 본 논문은 다양한 국가 연구보고서에서 사회문제와 해결방안을 자동으로 감지하기 위한 기계학습 모델을 구축하는 데에 필수적인 데이터셋을 제안하고자 한다. 우선 데이터를 구축하기 위해 사회문제와 쟁점을 다룬 연구보고서를 총 700건 수집하였다. 수집된 연구보고서에서 사회문제, 목적, 해결 방안 등 사회문제 해결과 관련된 내용이 담긴 문장을 추출 후 라벨링을 수행하였다. 또한 4개의 사전학습 언어모델을 기반으로 분류 모델을 구현하고 구축된 데이터셋을 통해 일련의 성능 실험을 수행하였다. 실험 결과 KLUE-BERT 사전학습 언어모델을 미세조정한 모델이 정확도 75.853%, F1 스코어 63.503%로 가장 높은 성능을 보였다.

• 한국의류학회지
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• 제28권3_4호
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• pp.403-413
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• 2004

The purpose of this study was to investigate to wearing practices and consumer needs for stockings. 503 subjects were surveyed in 2002. Data were analyzed by frequency, $\chi$$^2$, ANONA(LSD) and factor analysis. The results were as follows; 1) Many subjects considered the colors, but a very few of them considered the components and handling signs when purchase their stockings. These tendencies show meaningful differences according to their ages, jobs and the status of whether they are married or single. The wearing rates of stockings show differences according to their physical characteristics and social demographical variables. 2) They demand that the exact fiber contents and the mixture ratios which the stockings have should be indicated, and also want the sizes of the stockings to be indicated by three steps according to their body sizes, and they ask that the stockings have more various colors. They required that the stockings should have good ventilation, durability, warmth-keeping, hygroscopicity and elasticity in leg parts, and anti static electricity or bacteria.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2012년도 춘계 종합학술대회 논문집
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• pp.229-230
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• 2012

국내의 대형 기획사 및 대기업 위주의 유통사들 사이에서 음반제작, 유통, 판매 등에 들어가는 비용을 인디밴드가 감당하기란 어려운 일이다. 이러한 문제의 대안의 성격을 가지고 소셜 펀드레이징으로 음반제작을 지원하는 프로젝트가 출현했다. 소셜 펀드레이징은 불특정다수로부터 후원을 받아 아이디어나 프로젝트를 성사시키고, 그에 따른 보상을 제공하는 것을 말한다. 본 논문에서는 2011년과 2012년의 SYM(Support your music) 프로젝트를 사례로 삼아, 한국 인디밴드의 음반제작에 있어 소셜 펀드레이징이 가지는 의의를 분석한다. 이것이 기존의 음반시장에서 대안이 될 수 있는지, 또한 그 한계는 무엇인지 알아본다.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.641-651
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• 2023

Malicious hate speech and gender bias comments are common in online communities, causing social problems in our society. Gender bias and hate speech detection has been investigated. However, it is difficult because there are diverse ways to express them in words. To solve this problem, we attempted to detect malicious comments in a Korean hate speech dataset constructed in 2020. We explored bidirectional encoder representations from transformers (BERT)-based deep learning models utilizing hyperparameter tuning, data sampling, and logits ensembles with a label distribution. We evaluated our model in Kaggle competitions for gender bias, general bias, and hate speech detection. For gender bias detection, an F1-score of 0.7711 was achieved using an ensemble of the Soongsil-BERT and KcELECTRA models. The general bias task included the gender bias task, and the ensemble model achieved the best F1-score of 0.7166.