Academic Freedom and University Autonomy

This chapter examines the law in relation to the doctrines of university autonomy and academic freedom, in the Australian context. It first considers some traditional misconceptions and surrounding these doctrines, which seem to have obscured the real nature of the relationship between universities and the state. It then examines some laws and legal instruments at an international, federal and State level which define and regulate these freedoms. It considers some contemporary controversies, to illustrate both the strengths and weaknesses surrounding how alleged infringements of academic freedom and independence have been managed. It concludes with a look at an important emerging challenge which has implications for how we might avoid and manage such controversies in the future.

Planning, policy and you

The policy environment for regional natural resource management (NRM) has shifted considerably since it was first introduced in the early 2000s. This workshop will explore the impact of current policy on NRM planning and action through presentations and workshop discussion. To set the scene for the workshop discussion presentations will consider: 1) the impact of evolving national and state NRM policy in Australia; 2) how Australian NRM compares to other countries; 3) governance risks to NRM delivery; and 4) regional responses to NRM delivery. During the workshop element, participants from across regions will share their experiences and explore implications of current policy on the business of regional NRM.

Differences in identifying healthcare associated infections using clinical vignettes and the influence of respondent characteristics: A cross-sectional survey of Australian infection prevention staff

Background Australia has commenced public reporting and benchmarking of healthcare associated infections (HAIs), despite not having a standardised national HAI surveillance program. Annual hospital Staphylococcus aureus bloodstream (SAB) infection rates are released online, with other HAIs likely to be reported in the future. Although there are known differences between hospitals in Australian HAI surveillance programs, the effect of these differences on reported HAI rates is not known. Objective To measure the agreement in HAI identification, classification, and calculation of HAI rates, and investigate the influence of differences amongst those undertaking surveillance on these outcomes. Methods A cross-sectional online survey exploring HAI surveillance practices was administered to infection prevention nurses who undertake HAI surveillance. Seven clinical vignettes describing HAI scenarios were included to measure agreement in HAI identification, classification, and calculation of HAI rates. Data on characteristics of respondents was also collected. Three of the vignettes were related to surgical site infection and four to bloodstream infection. Agreement levels for each of the vignettes were calculated. Using the Australian SAB definition, and the National Health and Safety Network definitions for other HAIs, we looked for an association between the proportion of correct answers and the respondents’ characteristics. Results Ninety-two infection prevention nurses responded to the vignettes. One vignette demonstrated 100 % agreement from responders, whilst agreement for the other vignettes varied from 53 to 75 %. Working in a hospital with more than 400 beds, working in a team, and State or Territory was associated with a correct response for two of the vignettes. Those trained in surveillance were more commonly associated with a correct response, whilst those working part-time were less likely to respond correctly. Conclusion These findings reveal the need for further HAI surveillance support for those working part-time and in smaller facilities. It also confirms the need to improve uniformity of HAI surveillance across Australian hospitals, and raises questions on the validity of the current comparing of national HAI SAB rates.

Mathematical modeling for improved greenhouse gas balances, agro-ecosystems, and policy development: Lessons from the Australian experience

If the land sector is to make significant contributions to mitigating anthropogenic greenhouse gas (GHG) emissions in coming decades, it must do so while concurrently expanding production of food and fiber. In our view, mathematical modeling will be required to provide scientific guidance to meet this challenge. In order to be useful in GHG mitigation policy measures, models must simultaneously meet scientific, software engineering, and human capacity requirements. They can be used to understand GHG fluxes, to evaluate proposed GHG mitigation actions, and to predict and monitor the effects of specific actions; the latter applications require a change in mindset that has parallels with the shift from research modeling to decision support. We compare and contrast 6 agro-ecosystem models (FullCAM, DayCent, DNDC, APSIM, WNMM, and AgMod), chosen because they are used in Australian agriculture and forestry. Underlying structural similarities in the representations of carbon flows though plants and soils in these models are complemented by a diverse range of emphases and approaches to the subprocesses within the agro-ecosystem. None of these agro-ecosystem models handles all land sector GHG fluxes, and considerable model-based uncertainty exists for soil C fluxes and enteric methane emissions. The models also show diverse approaches to the initialisation of model simulations, software implementation, distribution, licensing, and software quality assurance; each of these will differentially affect their usefulness for policy-driven GHG mitigation prediction and monitoring. Specific requirements imposed on the use of models by Australian mitigation policy settings are discussed, and areas for further scientific development of agro-ecosystem models for use in GHG mitigation policy are proposed.