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In times of crisis, such as the Covid-19 global pandemic, journalists who write about biomedical information must have the strategic aim to be clearly and easily understood by everyone. In this study, we assume that journalistic discourse could benefit from language redundancy to improve clarity and simplicity aimed at science popularization. The concept of language redundancy is theoretically discussed with the support of discourse analysis and information theory. The methodology adopted is a corpus-based qualitative approach. Two corpora samples with Brazilian Portuguese (BP) texts on Covid-19 were collected. One with texts from a monthly science digital magazine called Pesquisa FAPESP aimed at students and researchers for scientific information dissemination and the other with popular language texts from a news Portal G1 (Rede Globo) aimed at unspecified and/or non-specialized readers. The materials were filtered with two descriptors: “vaccine” and “test.” Preliminary analysis of examples from these materials revealed two categories of redundancy: paraphrastic and polysemic. Paraphrastic redundancy is based on concomitant language reformulation of words, sentences, text excerpts, or even larger units. Polysemic redundancy does not easily show material evidence, but is based on cognitively predictable semantic association in socio-cultural domains. Both kinds of redundancy contribute, each in their own way, to improving text readability for science popularization in Brazil.

The study examines sustainability reporting in the government and not-for-profit organizations (GNFPs). Using a descriptive approach, data from the Global Reporting Initiative (GRI) are utilized to identify GNFP’s sustainability reporting trends and incentives over the period from 2001 to 2016. The study shows improvement in the GNFPs’ sustainability reporting over the analysis period, especially by larger organizations. In specific, results show that the number of GNFPs that reported has increased over the analysis period, and the number of social, economic, and environmental issues that are reported on has also increased although fragmentally across different GNFPs. In addition, a few GNFPs integrate their sustainability report with their financial report or obtain external assurance. The study shows that GNFPs’ sustainability reporting is motivated by meeting stakeholders’ needs and achieving business goals. Based on these findings, the study identifies future reporting opportunities for GNFPs to improve informativeness and reliability of sustainability reporting with the ultimate goals of improving transparency and accountability. The data used in this study capture only the GNFPs that reported or registered in the GRI database. Thus, future studies may use other data sets or conduct field and case analyses to obtain further insights into the process of adopting and reporting on sustainability and the roles that different stakeholders play in pursuing such efforts. In addition, the study identifies other future research opportunities. The study contributes to the extant literature on sustainability and social responsibility during periods of changing regulatory framework in less-researched organizations that contributes significantly to society.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.