Truth of the Trains

This article discusses the orphan trains movement of the late 19th century and also discusses whether or not a more personal or objective approach to studying history is more accurate

Rubidium-based Atomic Clock

In this paper we will explore the process of building an atomic clock from a function generator, go into an in-depth introductory discussion of the Datum LPRO, and examine how rubidium function generators work

The word order of subordinate clauses

Thesis (M.A.)--University of Kansas, Latin and Greek, 1918. ; Includes bibliographical references

Stakeholder engagement in marketing

It has long been recognised within the stakeholder management literature that value is enhanced through meaningful stakeholder relationships based on trust, commitment, loyalty and transparency. This resonates with developments within the marketing literature whereby the organisation-centric, transaction-based, buyer-supplier dyad focus of mainstream thinking has faced criticism for failing to understand the complex stakeholder networks that create and destroy value. Relational-based co-creation associated with relationship marketing, and the holistic approach embedded within stakeholder marketing, specifically address such criticisms. These represent an exciting new frontier for marketers. This chapter aims to add to the stakeholder marketing literature through the development of a marketing ladder of stakeholder engagement. The ladder of stakeholder management and engagement proposed by Friedman and Miles (2006) is reconfigured to reflect contemporary thought in relation to how a closer consideration of stakeholder management techniques can help to build trust and foster loyalty within the marketing function

The boundary of sustainability reporting: Evidence from the FTSE100

Purpose. The aim of this paper is to use a multidisciplinary theoretical understanding of boundary setting to develop a quadripartite model in which sustainability reporting boundaries are classified as ‘Reputation Management’, ‘Ownership and Control’, ‘Accountability; and, ‘Stakeholder Engagement’. Content analysis is then used to empirically test the model. Design/approach. Using impression management theory, rationalism, systems and contingency theory, and network theory a model is created which classifies sustainability reporting boundaries. Content analysis is used to empirically test boundaries across the disclosure of 49 GRI topics by the FTSE100. Findings. Sustainability reporting fails to discharge accountability due to adoption of narrow ‘Reputation Management’ boundaries. Boundaries are significantly (p<0.0001) narrower than previous research suggests. Findings support Impression Management Theory as the strongest theory to predict reporting content. An Ownership and Control boundary, although widely criticised, represents the boundary of progressive reporters, lending marginal support for economic theories. Accountability boundaries are scarce. No evidence was found for Stakeholder Engagement boundaries. Practical Implications. The determination of boundary is critical to the discharge of accountability. A critical consideration of boundary setting is required, including authentic stakeholder engagement in determining boundaries and transparency of boundary adopted. The results are ranked to enable benchmarking of the FTSE100. Boundaries can be widened through regulation or ‘name and shame campaigns’. Originality/value. This paper provides a theory-informed advancement in thinking on sustainability reporting boundary setting and the importance of this for advancing sustainability reporting quality

Meta-learning for data summarization based on instance selection method

The purpose of instance selection is to identify which instances (examples, patterns) in a large dataset should be selected as representatives of the entire dataset, without significant loss of information. When a machine learning method is applied to the reduced dataset, the accuracy of the model should not be significantly worse than if the same method were applied to the entire dataset. The reducibility of any dataset, and hence the success of instance selection methods, surely depends on the characteristics of the dataset, as well as the machine learning method. This paper adopts a meta-learning approach, via an empirical study of 112 classification datasets from the UCI Repository [1], to explore the relationship between data characteristics, machine learning methods, and the success of instance selection method.<br /

Egalitarian Teams in Action: Organizing for Library Initiatives

In 2006 Peter Senge, who coined the term the learning organization, wrote, “As the world becomes more interconnected and business becomes more complex and dynamic, work must become more ‘learningful’... It’s just not possible any longer to to figure it out from the top, and have everyone else following the orders of the ‘grand strategist’” (p. 4). Senge documented the need for professions and organizations that can change, that can quickly adapt, be nimble, learn, and find new opportunities in the changing information landscape. Libraries are not immune from this kind of pressure. In this case study, first presented at the 2017 LACUNY Institute, three library faculty members describe a team with the salient characteristics of commitment and nimbleness, a team that aims to be this new, “non-traditional” team, one that is in alignment with best practices for change management and learning organizations, and with the work of Etienne Wenger and others on Communities of Practice (CoPs). After describing the team’s background and formation, this case study presents the results of a mid-year survey, along with a list of the team’s work and accomplishments, as evidence of productivity and team members’ satisfaction. Specific benefits and challenges of the team’s structure and processes are discussed. Finally, best practices for this type of committed and agile teamwork are drawn from the CoP literature and this case study, and some of the ways this “learningful” experience may impact faculty as individuals, and what that may mean for the future of the library, are considered

'Rorting the system' : police detectives, diversity, and workplace advantage

Internal workplace practices and policies in policing are based on a notion of fairness and equal opportunity. Yet police organizations are frequently criticized for discriminatory policing practices, unfair and biased workplace practices, and poor interpersonal treatment of officers. Whilst there is a wide body of research examining diversity in relation to external police practices, there is a lack of knowledge regarding diversity and internal workplace practices; particularly from the perspective of police detectives who often have more substantial policing experience and longer employment histories than other non-commissioned officers. Contributing new findings to the extant policing literature, this research analyzes data collected from interviews with twenty police detectives working in one of the largest Australian police organizations. It suggests that police detectives in this study have negative perceptions of diversity, and associate diversity with unfair advantages in the workplace. In Australian culture, the phrase ‘rorting the system’ is an informal expression used to describe individuals or groups of people who take unfair advantage of a public service or workplace policy to change their circumstances. The findings suggest that detectives in this study believe diversity enables some officers to take advantage of workplace policy and ‘rort’ the system

Towards objective measures of algorithm performance across instance space

This paper tackles the difficult but important task of objective algorithm performance assessment for optimization. Rather than reporting average performance of algorithms across a set of chosen instances, which may bias conclusions, we propose a methodology to enable the strengths and weaknesses of different optimization algorithms to be compared across a broader instance space. The results reported in a recent Computers and Operations Research paper comparing the performance of graph coloring heuristics are revisited with this new methodology to demonstrate (i) how pockets of the instance space can be found where algorithm performance varies significantly from the average performance of an algorithm; (ii) how the properties of the instances can be used to predict algorithm performance on previously unseen instances with high accuracy; and (iii) how the relative strengths and weaknesses of each algorithm can be visualized and measured objectively