Drivers of Agricultural Entrepreneurship in Factor-Driven Economies

This piece of research aims to explain the drivers of early-stage entrepreneurship in factor-driven economies and how these are affected by several cognitive factors. This study covers literature on several driving factors of entrepreneurial activity, trying to formulate a framework of determinants of early-stage agricultural entrepreneurial activity. For this purpose, the adult population survey (APS) data of factor-driven economies published by GEM has been used. The selected respondents (848) include those individuals who, alone or with other individuals, presently involved in venture creation, including any self-employment in the agricultural sector. The impact of cognitive and social capital factors on early-stage entrepreneurial activity is measured using logistic regression. The findings suggest that its opportunity perception and self-efficacy, which are the major motivators of early-stage entrepreneurship in developing nations. Also, there are gender biases and age-related negativity with respect to new agri-business creation in developing countries.

MOOCs as a Pedagogical and Strategic Tool in the Implementation of Institutional Policies Related to Gender Inequality in Educational Institutions

As a privileged means of socialization, the school's mission is to promote equal opportunities and educate for the values of pluralism and gender equality. The introduction of a gender perspective in educational policies is a fundamental tool to fight gender inequalities. This chapter aims to present a theoretical-methodological proposal for the development of a massive open online course (MOOC) addressed to those interested in learning and studying about gender inequalities and women's empowerment. The MOOC will introduce inspiring examples of feminine resistance and resilience from Portugal and Brazil, showing women who have fought for the rights and policies for gender equality, against gender biases, and building women's citizenship in and through education. The integration of MOOCs as an educational tool raises questions and challenges both in the didactic-pedagogical forum and about institutional policies.

Femininity and Masculinity in Arabic Words: Gender Marking in Muslim Cosmology

The Modern Standard Arabic (MSA) language strongly indicates the sociolinguistic phenomenon as it reflects gender marking in language use. This study aims to explore how the Arabic letters attributed to specific gender identities, how the gender ideology of Arab culture create gender biases, and how the biases influence Arab social structure. It uses aspects of masculinity and femininity of Arabic letters that affect gender inequality and order of values on language, tradition and culture. Masculine letters are letters that have the property of being able to hold and entail other letters, while feminine letters that have the nature can be attached with other letters but cannot be attached. In this study, Arabic letters were mapped by observing their use in written and oral interaction in the contexts of Arab as first and second language. This research is a qualitative in nature. The data on ideology's influence on social structure were collected through interviews with three key informants representing their areas of expertise on language anthropology, sociolinguistic, and applied linguistic. The morphological analysis was carried out to identify the internal structure of the words. The sociolinguistic analysis explored the linguistic construction that to social construction. The finding showed that their internal structures, these letters were classified as masculine or feminine. From the sociolinguistic point of view, gender issues following social construction that has already formed gender relations. In other words, Arabic letters affect the order of values that tend to be gender-biased in the Arabic context.

What Might Books Be Teaching Young Children About Gender?

We investigated how gender is represented in children’s books using a novel 200,000-word corpus comprising 247 popular, contemporary books for young children. Using adult human judgments and word co-occurrence data, we quantified gender biases of words in individual books and in the whole corpus. We found that children’s books contain many words that adults judge as gendered. Semantic analyses based on co-occurrence data yielded word clusters related to gender stereotypes (e.g., feminine: emotions; masculine: tools). Co-occurrence data also indicated that many books instantiate gender stereotypes identified in other research (e.g., girls are better at reading, and boys are better at math). Finally, we used large-scale data to estimate the gender distribution of the audience for individual books, and we found that children are more often exposed to stereotypes for their own gender. Together, the data suggest that children’s books may be an early source of gender associations and stereotypes.

Towards women-inclusive ecology: Representation, behavior, and perception of women at an international conference

Conferences are ideal platforms for studying gender gaps in science because they are important cultural events that reflect barriers to women in academia. Here, we explored women’s participation in ecology conferences by analyzing female representation, behavior, and personal experience at the 1st Meeting of the Iberian Society of Ecology (SIBECOL). The conference had 722 attendees, 576 contributions, and 27 scientific sessions. The gender of attendees and presenters was balanced (48/52% women/men), yet only 29% of the contributions had a woman as last author. Moreover, men presented most of the keynote talks (67%) and convened most of the sessions. Our results also showed that only 32% of the questions were asked by women, yet the number of questions raised by women increased when the speaker or the convener was a woman. Finally, the post-conference survey revealed that attendees had a good experience and did not perceive the event as a threatening context for women. Yet, differences in the responses between genders suggest that women tended to have a worse experience than their male counterparts. Although our results showed clear gender biases, most of the participants of the conference failed to detect it. Overall, we highlight the challenge of increasing women’s scientific leadership, visibility and interaction in scientific conferences and we suggest several recommendations for creating inclusive meetings, thereby promoting equal opportunities for all participants.

Questions from a Contraceptive Pill Junkie: Applying Human Psychometrics to Investigate Gender Bias in Machine Learning

<p>With the rapid uptake of machine learning artificial intelligence in our daily lives, we are beginning to realise the risks involved in implementing this technology in high-stakes decision making. This risk is due to machine learning decisions being based in human-curated datasets, meaning these decisions are not bias-free. Machine learning datasets put women at a disadvantage due to factors including (but not limited to) historical exclusion of women in data collection, research, and design; as well as the low participation of women in artificial intelligence fields. These factors mean that applications of machine learning may fail to treat the needs and experiences of women as equal to those of men. Research into understanding gender biases in machine learning frequently occurs within the computer science field. This has frequently resulted in research where bias is inconsistently defined, and proposed techniques do not engage with relevant literature outside of the artificial intelligence field. This research proposes a novel, interdisciplinary approach to the measurement and validation of gender biases in machine learning. This approach translates methods of human-based gender bias measurement in psychology, forming a gender bias questionnaire for use on a machine rather than a human. The final output system of this research as a proof of concept demonstrates the potential for a new approach to gender bias investigation. This system takes advantage of the qualitative nature of language to provide a new way of understanding gender data biases by outputting both quantitative and qualitative results. These results can then be meaningfully translated into their real-world implications.</p>

Questions from a Contraceptive Pill Junkie: Applying Human Psychometrics to Investigate Gender Bias in Machine Learning

<p>With the rapid uptake of machine learning artificial intelligence in our daily lives, we are beginning to realise the risks involved in implementing this technology in high-stakes decision making. This risk is due to machine learning decisions being based in human-curated datasets, meaning these decisions are not bias-free. Machine learning datasets put women at a disadvantage due to factors including (but not limited to) historical exclusion of women in data collection, research, and design; as well as the low participation of women in artificial intelligence fields. These factors mean that applications of machine learning may fail to treat the needs and experiences of women as equal to those of men. Research into understanding gender biases in machine learning frequently occurs within the computer science field. This has frequently resulted in research where bias is inconsistently defined, and proposed techniques do not engage with relevant literature outside of the artificial intelligence field. This research proposes a novel, interdisciplinary approach to the measurement and validation of gender biases in machine learning. This approach translates methods of human-based gender bias measurement in psychology, forming a gender bias questionnaire for use on a machine rather than a human. The final output system of this research as a proof of concept demonstrates the potential for a new approach to gender bias investigation. This system takes advantage of the qualitative nature of language to provide a new way of understanding gender data biases by outputting both quantitative and qualitative results. These results can then be meaningfully translated into their real-world implications.</p>