Australian employees’ attitudes towards Unions

- Between 1996 and 2002, Australian employees’ attitudes towards unions have become more positive - In 2002, only 17 per cent of employees thought that Australia would be better off without unions compared to 25 per cent in 1996 - There has been a significant decline in the proportion of employees who think that unions in Australia do not look after their members (29 per cent in 2002 down from 43 per cent in 1996) - The perception that management has more power than unions has remained the same - The percentage of employees who would rather be in a union has remained around the 50 per cent mark - Some of the attitudes towards unions of male employees, older workers aged 45 plus, younger employees aged 18 to 24 and those in larger organisations have improved relative to other employees - 78 per cent of employees believe that executive pay rises should be linked to workers’ pay rises - 58 per cent of employees think that organisations in Australia conduct their business in an ethical and proper way - About 40 per cent of employees who join unions do so for a ‘safety net’ - Non membership of a union appears to be related to inertia or indifference rather than to ideological opposition to union

Polarizing Political Polls: How Visualization Design Choices Can Shape Public Opinion and Increase Political Polarization

While we typically focus on data visualization as a tool for facilitating cognitive tasks (e.g., learning facts, making decisions), we know relatively little about their second-order impacts on our opinions, attitudes, and values. For example, could design or framing choices interact with viewers' social cognitive biases in ways that promote political polarization? When reporting on U.S. attitudes toward public policies, it is popular to highlight the gap between Democrats and Republicans (e.g., with blue vs red connected dot plots). But these charts may encourage social-normative conformity, influencing viewers' attitudes to match the divided opinions shown in the visualization. We conducted three experiments examining visualization framing in the context of social conformity and polarization. Crowdworkers viewed charts showing simulated polling results for public policy proposals. We varied framing (aggregating data as non-partisan "All US Adults," or partisan "Democrat" and "Republican") and the visualized groups' support levels. Participants then reported their own support for each policy. We found that participants' attitudes biased significantly toward the group attitudes shown in the stimuli and this can increase inter-party attitude divergence. These results demonstrate that data visualizations can induce social conformity and accelerate political polarization. Choosing to visualize partisan divisions can divide us further

Using Bayesian networks to represent parameterised risk models for the UK railways

PhDThe techniques currently used to model risk and manage the safety of the UK railway network are not aligned to the mechanism by which catastrophic accidents occur in this industry. In this thesis, a new risk modelling method is proposed to resolve this problem. Catastrophic accidents can occur as the result of multiple failures occurring to all of the various defences put in place to prevent them. The UK railway industry is prone to this mechanism of accident occurrence, as many different technical, operational and organizational defences are used to prevent accidents. The railway network exists over a wide geographic area, with similar accidents possible at many different locations. The risk from these accidents is extremely variable and depends on the underlying conditions at each particular location, such as the state of assets or the speed of trains. When unfavourable conditions coincide the probability of multiple failures of planned defences increases and a 'risk hotspot' arises. Ideal requirements for modelling risk are proposed, taking account of the need to manage multiple defences of conceptually different type and the existence of risk hotspots. The requirements are not met by current risk modelling techniques although some of the requirements have been addressed experimentally, and in other industries and countries. It is proposed to meet these requirements using Bayesian Networks to supplement and extend fault and event tree analysis, the traditional techniques used for risk modelling in the UK railway industry. Application of the method is demonstrated using a case study: the building of a model of derailment risk on the UK railway network. The proposed method provides a means of better integrating industry wide analysis and risk modelling with the safety management tasks and safety related decisions that are undertaken by safety managers in the industry

Harmonising Safety Management Systems in the European Railway Sector

The European Commission has set railway policy to encourage the liberalisation of the railway industry across all European Union member states. A single market for railway services is envisaged as a means to improve the competitiveness of the railways with other modes of transportation. A key piece of the legislation implemented in response to this policy, is the Railway Safety Directive. This legislation recognises that it is not just technical and procedural harmonisation that is required to create an effective internal market for railway services in Europe. Safety Management practices must also be harmonised. One of the elements of the directive is the requirements that all Railway Undertakings (who run train services) and Infrastructure Managers (who maintain the railway network) in European members states implement a Safety Management System which meets certain criteria and is certified by the relevant National Safety Authority. The European research projects SAMRAIL (Safety Management in Railways) and SAMNET (Safety Management and Interoperability Thematic Network for Railways) were funded by the Commission to investigate and to propose practical approaches to help implementation of the requirements in the Railway Safety Directives. As part of this research detailed guidance on how to structure and implement a Safety Management System that was suitable for and compatible with Europe's future railway environment was produced. In this paper we describe those aspects of the Railway Safety Directive relating to Safety Management Systems, including the proposed certification requirements, and outline the proposals made by SAMRAIL and SAMNET for effectively implementing safety management systemsRESEAU FERROVIAIRE;LEGISLATION;EUROPE;STANDARDISATION;SECURITE

Causal Modelling of Lower Consequence Rail Safety Incidents

Waiting for copyright information from publisherThe Safety Risk Model (SRM) is a key source of information for the GB rail industry. It is a structured representation of the 120 hazardous events that can lead to injury or death during the operation of the railway and is used to estimate the risk to passengers, workers and third parties. The SRM includes both rare but high consequence events such as train collisions and more frequent but lower consequence events such as passenger accidents at stations. In aggregate, these lower consequence events make an important contribution to the overall risk, which is measured by a weighted sum of injuries of different severity. Where possible, the SRM is derived from historical incident data, but the derivation of the model parameters still present challenges, which differ for different subsets of events. High consequence events occur rarely so it is necessary to use expert judgement in detailed models of these incidents. In comparison, the low consequence events occur more frequently, but both records of incidents and the models in the SRM are less detailed. The frequency of these low consequence events is sufficient to allow both the absolute risk and trends in the overall risk to be monitored directly. However, without explicit causal factors in the data or the model, the models are less able to support risk management directly, since this requires estimates of the risk reduction possible from particular interventions and control measures. Moreover, such estimates must be made locally, taking account of the local conditions, and at each location even the low consequence events are infrequent. In this paper we describe an approach to modelling the causes of low consequence events in a way that supports the management of risk. We show both how to extract more information from the available data and how to make use of expert judgement about contributory factors. Our approach uses Bayesian networks: we argue their advantages over fault and event trees for modelling incidents that have many contributory causes. Finally, we show how the new approach improves safety management, both by estimating the contribution of the underlying causes to this risk and by predicting how possible management interventions and control measures would reduce this risk

Texas Cities in the Era of Government Transparency

We are in the Era of Government Transparency. Recently, politicians from President Barack Obama to Texas Governor Rick Perry have touted a commitment to openness and transparency in their respective administrations. Citizens have also embraced the idea. No longer content with viewing the government as a mysterious black box where taxes go in and services come out, taxpayers today expect, and in some cases demand, to know how decisions are made. As discussions ensue about growing distrust between citizens and government, increased transparency can offer a way to bridge this divide. Clear, organized and useful data posted online is a good indicator of a city’s transparency. It is best for city governments to engage their citizens in a dialogue about what information the public wants and what format will encourage them to best use it

Functional genomics of mountain pine beetle (Dendroctonus ponderosae) midguts and fat bodies

<p>Abstract</p> <p>Background</p> <p>The mountain pine beetle (<it>Dendroctonus ponderosae</it>) is a significant coniferous forest pest in western North America. It relies on aggregation pheromones to colonize hosts. Its three major pheromone components, <it>trans</it>-verbenol, <it>exo</it>-brevicomin, and frontalin, are thought to arise via different metabolic pathways, but the enzymes involved have not been identified or characterized. We produced ESTs from male and female midguts and associated fat bodies and used custom oligonucleotide microarrays to study gene expression patterns and thereby made preliminary identification of pheromone-biosynthetic genes.</p> <p>Results</p> <p>Clones from two un-normalized cDNA libraries were directionally sequenced from the 5' end to yield 11,775 ESTs following sequence cleansing. The average read length was 550 nt. The ESTs clustered into 1,201 contigs and 2,833 singlets (4,034 tentative unique genes). The ESTs are broadly distributed among GO functional groups, suggesting they reflect a broad spectrum of the transcriptome. Among the most represented genes are representatives of sugar-digesting enzymes and members of an apparently Scolytid-specific gene family of unknown function. Custom NimbleGen 4-plex arrays representing the 4,034 tentative unique genes were queried with RNA from eleven different biological states representing larvae, pupae, and midguts and associated fat bodies of unfed or fed adults. Quantitative (Real-Time) RT-PCR (qRT-PCR) experiments confirmed that the microarray data accurately reflect expression levels in the different samples. Candidate genes encoding enzymes involved in terminal steps of biosynthetic pathways for <it>exo</it>-brevicomin and frontalin were tentatively identified.</p> <p>Conclusions</p> <p>These EST and microarray data are the first publicly-available functional genomics resources for this devastating forestry pest.</p

Why Risk Models should be Parameterised

Risk models using fault and event trees can be extended with explicit factors, which are states of the system, its users or its environment that influence event probabilities. The factors act as parameters in the risk model, enabling the model to be re-used and also providing a new way to estimate the overall risk of a system with many instances of the risk. A risk model with parameters can also be clearer

Party patronage in contemporary democracies: results from an expert survey in 22 countries from five regions

This Research Note presents a new dataset of party patronage in 22 countries from five regions. The data was collected based on the same methodology to compare patterns of patronage within countries, across countries and across world regions that are usually studied separately. The Note addresses three research questions that are at the center of debates on party patronage, which is understood as the power of political parties to make appointments to the public and semi-public sector: the scope of patronage, the underlying motivations, and the criteria on the basis of which appointees are selected. The exploration of the dataset shows that party patronage is, to a different degree, widespread across all regions. The data further shows differences between policy areas, types of institutions such as government ministries, agencies and state-owned enterprises, and higher, middle and lower ranks of the bureaucracy. It is demonstrated that the political control of policy-making and implementation is the most common motivation for making political appointments. However, in countries with a large scope of patronage, appointments serve the purpose of both political control and rewarding supporters in exchange for votes and services. Finally, the data shows that parties prefer to select appointees who are characterized by political and personal loyalty as well as professional competence