Reflective Writing in Teacher Education in China: Insights from Systemic Functional Linguistics

This article aims to demonstrate the application of theory to teaching practice by examining how two teacher educators drew upon systemic functional linguistics (SFL) as a pedagogical and analytical tool to inform their teaching of reflective writing to Mandarin-dominant teacher candidates enrolled in an ESOL course within an educator preparation program at a university using English Medium Instruction (EMI) in China. First, the authors describe how they incorporated the SFL appraisal framework into their teaching to bring their multilingual student writers’ attention to the language of evaluation as they prepared to construct reflective language learning autobiographies. Second, the authors demonstrate how SFL-informed text analysis of the appraisal resources used within students’ written reflections deepened their understanding of their students’ reflective writing practices and informed their teaching and course development. The article concludes with suggestions for using the SFL appraisal framework to support the reflective writing of their multilingual teacher candidates

Are mice good models for human neuromuscular disease? Comparing muscle excursions in walking between mice and humans

The mouse is one of the most widely used animal models to study neuromuscular diseases and test new therapeutic strategies. However, findings from successful pre-clinical studies using mouse models frequently fail to translate to humans due to various factors. Differences in muscle function between the two species could be crucial but often have been overlooked. The purpose of this study was to evaluate and compare muscle excursions in walking between mice and humans

Colloquium on Solid State Devices

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Signaling in Secret: Pay-for-Performance and the Incentive and Sorting Effects of Pay Secrecy

Key Findings: Pay secrecy adversely impacts individual task performance because it weakens the perception that an increase in performance will be accompanied by increase in pay; Pay secrecy is associated with a decrease in employee performance and retention in pay-for-performance systems, which measure performance using relative (i.e., peer-ranked) criteria rather than an absolute scale (see Figure 2 on page 5); High performing employees tend to be most sensitive to negative pay-for- performance perceptions; There are many signals embedded within HR policies and practices, which can influence employees’ perception of workplace uncertainty/inequity and impact their performance and turnover intentions; and When pay transparency is impractical, organizations may benefit from introducing partial pay openness to mitigate these effects on employee performance and retention

Explicit Relative Performance Evaluation in Performance-Vested Equity Grants

Using data from FTSE 350 firms, we examine factors influencing explicit relative performance evaluation (RPE) conditions in performance-vested equity grants. We provide exploratory evidence on whether the use or characteristics of RPE are associated with efforts to improve incentives by removing common risk, other economic factors discussed in the RPE literature, or external pressure to implement RPE. We find that many of these economic factors, including common risk reduction, are more closely related to specific relative performance conditions than to the firm-level decision to use RPE in some or all of their equity grants. We also find that greater external monitoring by institutional investors or others is associated with plans with tougher overall RPE conditions. The relative performance conditions are binding in most RPE plans, with nearly two-thirds of the grants vesting only partially or not vesting at all. Further, we find evidence that vesting percentages vary in RPE and non-RPE plans

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Development time and new product sales: A contingency analysis of product innovativeness and price

Opposing theories and conflicting empirical results with regard to the effect of development time on new product sales suggest the need for a contingency analysis into factors affecting this relationship. This study uses a unique combination of accounting and perceptual data from 129 product development projects to test the combined contingency effect of product innovativeness and new product price on the relationship between development time and new product sales. The results show that for radically new products with short development times, price has no effect on new product sales. When the development time is long, price has a negative effect on the sales of radical new products. The findings additionally show that price has no effect on sales for incremental new products with short development times and a negative effect for incremental new products with long development times. Together, these findings shed new light on the relationship between development time and new product sales

Amyotrophic lateral sclerosis and frontotemporal dementia: distinct and overlapping changes in eating behaviour and metabolism.

Metabolic changes incorporating fluctuations in weight, insulin resistance, and cholesterol concentrations have been identified in several neurodegenerative disorders. Whether these changes result from the neurodegenerative process affecting brain regions necessary for metabolic regulation or whether they drive the degenerative process is unknown. Emerging evidence from epidemiological, clinical, pathological, and experimental studies emphasises a range of changes in eating behaviours and metabolism in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). In ALS, metabolic changes have been linked to disease progression and prognosis. Furthermore, changes in eating behaviour that affect metabolism have been incorporated into the diagnostic criteria for FTD, which has some clinical and pathological overlap with ALS. Whether the distinct and shared metabolic and eating changes represent a component of the proposed spectrum of the two diseases is an intriguing possibility. Moreover, future research should aim to unravel the complex connections between eating, metabolism, and neurodegeneration in ALS and FTD, and aim to understand the potential for targeting modifiable risk factors in disease development and progression.This work was supported by funding to Forefront, a collaborative research group dedicated to the study of frontotemporal dementia and motor neurone disease, from the National Health and Medical Research Council of Australia (NHMRC) program grant (#1037746 to GH, MK and JH) and the Australian Research Council Centre of Excellence in Cognition and its Disorders Memory Node (#CE110001021 to OP and JH) and other grants/sources (NHMRC project grant #1003139). We are grateful to the research participants involved with the ForeFront research studies. RA is a Royal Australasian College of Physicians PhD scholar and MND Australia PhD scholar. MI is an ARC Discovery Early Career Researcher Award Fellow (#DE130100463). OP is an NHMRC Career Development Research Fellow (#1022684). GH is a NHMRC Senior Principal Research Fellow (#1079679). L.M.I. is a NHMRC Senior Research Fellow (#1003083).This is the author accepted manuscript. The final version is available from Elsevier at http://dx.doi.org/10.1016/S1474-4422(15)00380-4