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Geoengineering technologies – deliberate, large-scale interventions to alter the Earth's climate – present an opportunity to ameliorate the effects of climate change; an opportunity policy-makers are beginning to consider. However, the safety and efficacy of geoengineering strategies is uncertain, and there is concern advancing these technologies engenders a range of non-physical risks; in particular, moral hazard. In economics, moral hazard occurs when insured agents no longer bear the full consequences of risk-taking, and consequentially increase their exposure to risk. However, while certainly analogous, the way the term is used in the geoengineering literature is ambiguous, describing a patchwork of mechanisms of action, hazardous behaviours, and undesirable outcomes. Importantly, as moral hazard concerns are likely to inform policy-making and regulatory responses to these technologies, this unclear specification could impede scholarly, policy, and public debate. This article charts this ambiguity, documenting the range of meanings of moral hazard in the geoengineering literature. Results suggest moral hazard is used at least eight different ways in the literature, with the three most common frames being Insurance, Unwilling and Avoid. With this, we suggest that those articulating moral hazard concerns about geoengineering technologies work to articulate as clearly as possible what the problem actually is. [ABSTRACT FROM AUTHOR]

Can Australia's Ph.D. graduates be better utilised in the non-academic workforce? There has been a historic structural decline in the ability of Ph.D. graduates to find work within academia for the last couple of decades (Forsyth in A history of the modern Australian University, New South Press, Sydney, 2014). Around 60% of Ph.D. graduates in Australia, now find jobs outside the academy, and the number is growing year on year (McGagh et al. in Securing Australia's future: review of Australia's research training system, https://acola.org.au/wp/PDF/SAF13/SAF13%20RTS%20report.pdf, 2016). The Ph.D. is a degree designed in the early twentieth century to credential the academic workforce. How to make it fit contemporary needs, when many, if not most, graduates do not work in academia, is a question that must be addressed by higher education managers and policymakers. Progress has been slow, partly because of the lack of reliable data-driven evidence to inform this work. This paper puts forward a novel hybrid quantitative/qualitative approach to the problem of analysing Ph.D. employability. We report on a project using machine learning (ML) and natural language processing to perform a 'big data' analysis on the text content of non-academic job advertisements. This paper discusses the use of ML in this context and its future utility for researchers. Using these methods, we performed an analysis of the extent of demand for Ph.D. student skills and capabilities in the Australian employment market. We show how these new methods allow us to handle large, complex datasets, which are often left unexplored because of human labour costs. This analysis could be reproduced outside of the Australian context, given an equivalent dataset. We give an outline of our approach and discuss some of the advantages and limitations. This paper will be of interest for those involved in reshaping Ph.D. programs and anyone interested in exploring new ML methods to inform education policy work. [ABSTRACT FROM AUTHOR]

A common perception exists in the media that university instructors are a liberal elite who use their positions to instill a biased perspective in their students. Exploring student attitudes in the United States and Australia, the present study examined how students’ academic beliefs are related to their inferences of instructor ideological bias and the degree to which they identify as reacting to these inferences in the college classroom. Results of regression analysis using previously validated survey instruments revealed that students’ academic entitlement and grade orientation (GO) predicted perceptions of instructor bias. In addition, students’ GO predicted students’ reactions to such inferred bias. Institutional differences, pedagogical implications, and areas for future research are discussed. [ABSTRACT FROM PUBLISHER]

Hindu nationalism has had a significant impact on Indian politics over the last few decades. A crucial aspect of this ideology has been its production of a means of understanding the physical world. Taken on as part of a Hindu national identity, this understanding has great political power. This article examines this spatial understanding, augmenting theories of nationalism with works from the “spatial turn” in cultural analysis to explore the “lived space” of the Hindu nationalist. It is suggested that this spatial method can tell us much about what the ideology means for individuals and about its sources of power. [ABSTRACT FROM AUTHOR]

The article discusses the study on the social evidence of the impact of climate change in human society based from Google Books Ngram Viewer data. Topics discussed include the cultural analysis of the effects of climate change, the autocorrelation of temperature with the climate change effects, and the predicted climate change effects such as floods and droughts.

This study analyzed tweets from handles associated with the Russian Internet Research Agency in an effort to better understand the tactics employed by that organization on the social media platform Twitter in their attempt to influence U.S. political discourse and the outcome of the 2016 U.S. Presidential election. We sampled tweets from the month preceding the election and analyzed to understand the qualitative nature of these tweets as well as quantitative differences between how types of IRA Twitter accounts communicated. Seven categories of tweet behavior were identified: attack left, support right, attack right, support left, attack media, attack civil institutions, and camouflage. While camouflage was the most common type of tweet (52.6%), descriptive analyses showed it was followed by attack left (12%) and support right (7%). A variety of quantitative differences were shown between how account types behaved. • We sampled Russian Internet Research Agency tweets from the month preceding the 2016 U.S. Presidential election. • We analyzed tweets to understand IRA messaging as well as differences between how types of IRA accounts communicated. • Seven distinct categories of behavior were identified and categories were employed differently by account type. • We discuss implications for what findings mean relative to prevailing and competing narratives surrounding IRA activity. • Findings suggest the IRA was both sowing division while also supporting candidate Trump. [ABSTRACT FROM AUTHOR]

In this content analysis, we explored how students address instructor ideology in the university classroom through the social media platform Twitter. We employed Boolean search operators through Salesforce Marketing Cloud Radian6 software to gather tweets and identified English language tweets by how students referenced their instructor's ideology. Tweets originated in the United States (U.S.), the United Kingdom (U.K.), and Australia. Using qualitative coding procedures, we identified seven themes in the data that described how students wrote about instructor ideology on Twitter:venting regarding instructor's ideology,sharing classroom anecdote,affirming instructor's beliefs/actions,expressing grade concern,articulating an opposing view,reporting conflict, andindicating confusion. Across categories, Twitter was employed primarily as a back-stage means to communicate expressive dissent regarding instructor ideology and also to demonstrate students’ identity. We identified differences between categories based on the perceived ideology of the students’ instructors as well as differences in the number of tweets per capita originating in each country. [ABSTRACT FROM PUBLISHER]