The Australian Research Quality Framework: A live experiment in capturing the social, economic, environmental, and cultural returns of publicly funded research

Copyright @ 2008 Wiley Periodicals Inc. This is the accepted version of the following article: Donovan, C. (2008), The Australian Research Quality Framework: A live experiment in capturing the social, economic, environmental, and cultural returns of publicly funded research. New Directions for Evaluation, 2008: 47–60, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/ev.260/abstract.The author regards development of Australia's ill-fated Research Quality Framework (RQF) as a “live experiment” in determining the most appropriate approach to evaluating the extra-academic returns, or “impact,” of a nation's publicly funded research. The RQF was at the forefront of an international movement toward richer qualitative, contextual approaches that aimed to gauge the wider economic, social, environmental, and cultural benefits of research. Its construction and implementation sent mixed messages and created confusion about what impact is, and how it is best measured, to the extent that this bold live experiment did not come to fruition

Guest Editors’ Introduction: People Management and Emerging Market Multinationals

There is a very extensive body of literature on how multinationals manage their people in different national contexts. However, the bulk of this literature focuses on the case of multinationals from the advanced industrial economies, and to a considerable extent, the United States. Very much less has been written on multinationals with their country of origin being an emerging market, and what little there is has focused on a very limited number of preferred cases. The growing importance of emerging economies has led to an upsurge of strategy research on the topic (Wright, Filatotchev, Hoskisson, & Peng, 2005); however, research on human resource management has not paid enough attention to emerging market multinational enterprises (EMNEs). Many EMNEs tend to be smaller in size with considerably fewer resources and less international experience than their counterparts from developed markets, limiting their ability to transfer management practice across their subsidiaries (Thite, Wilkinson, & Shah, 2012), although there are important exceptions to this rule. This introductory article seeks to contribute to the emerging body of literature in this area, through seeking to encourage fresh insights, particularly on the varieties of people management encountered in different national contexts

Guest Editors’ Introduction: People Management and Emerging Market Multinationals

There is a very extensive body of literature on how multinationals manage their people in different national contexts. However, the bulk of this literature focuses on the case of multinationals from the advanced industrial economies, and to a considerable extent, the United States. Very much less has been written on multinationals with their country of origin being an emerging market, and what little there is has focused on a very limited number of preferred cases. The growing importance of emerging economies has led to an upsurge of strategy research on the topic (Wright, Filatotchev, Hoskisson, & Peng, 2005); however, research on human resource management has not paid enough attention to emerging market multinational enterprises (EMNEs). Many EMNEs tend to be smaller in size with considerably fewer resources and less international experience than their counterparts from developed markets, limiting their ability to transfer management practice across their subsidiaries (Thite, Wilkinson, & Shah, 2012), although there are important exceptions to this rule. This introductory article seeks to contribute to the emerging body of literature in this area, through seeking to encourage fresh insights, particularly on the varieties of people management encountered in different national contexts

Brokering innovation to better leverage R&D investment

What is the contribution of innovation brokers in leveraging research and development (R&D) investment to enhance industry-wide capabilities? The case of the Australian Cooperative Research Centre for Construction Innovation (CRC CI) is considered in the context of motivating supply chain firms to improve their organizational capabilities in order to acquire, assimilate, transfer and exploit R&D outcomes to their advantage, and to create broader industry and national benefits. A previous audit and analysis has shown an increase in business R&D investment since 2001. The role of the CRC CI in contributing to growth in the absorptive capacity of the Australian construction industry as a whole is illustrated through two programmes: digital modelling/building information modelling (BIM) and construction site safety. Numerous positive outcomes in productivity, quality, improved safety and competitiveness were achieved between 2001 and 2009

Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted. This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities. This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account

Ancillary human health benefits of improved air quality resulting from climate change mitigation

<p>Abstract</p> <p>Background</p> <p>Greenhouse gas (GHG) mitigation policies can provide ancillary benefits in terms of short-term improvements in air quality and associated health benefits. Several studies have analyzed the ancillary impacts of GHG policies for a variety of locations, pollutants, and policies. In this paper we review the existing evidence on ancillary health benefits relating to air pollution from various GHG strategies and provide a framework for such analysis.</p> <p>Methods</p> <p>We evaluate techniques used in different stages of such research for estimation of: (1) changes in air pollutant concentrations; (2) avoided adverse health endpoints; and (3) economic valuation of health consequences. The limitations and merits of various methods are examined. Finally, we conclude with recommendations for ancillary benefits analysis and related research gaps in the relevant disciplines.</p> <p>Results</p> <p>We found that to date most assessments have focused their analysis more heavily on one aspect of the framework (e.g., economic analysis). While a wide range of methods was applied to various policies and regions, results from multiple studies provide strong evidence that the short-term public health and economic benefits of ancillary benefits related to GHG mitigation strategies are substantial. Further, results of these analyses are likely to be underestimates because there are a number of important unquantified health and economic endpoints.</p> <p>Conclusion</p> <p>Remaining challenges include integrating the understanding of the relative toxicity of particulate matter by components or sources, developing better estimates of public health and environmental impacts on selected sub-populations, and devising new methods for evaluating heretofore unquantified and non-monetized benefits.</p