Effects of modified-implement training on fast bowling in cricket

The effects of training with overweight and underweight cricket balls on fast-bowling speed and accuracy were investigated in senior club cricket bowlers randomly assigned to either a traditional (n = 9) or modified-implement training (n = 7) group. Both groups performed bowling training three times a week for 10 weeks. The traditional training group bowled only regulation cricket balls (156 g), whereas the modified-implement training group bowled a combination of overweight (161-181 g), underweight (151-131 g) and regulation cricket balls. A radar gun measured the speed of 18 consecutive deliveries for each bowler before, during and after the training period. Video recordings of the deliveries were also analysed to determine bowling accuracy in terms of first-bounce distance from the stumps. Bowling speed, which was initially 108 +/- 5 km h(-1) (mean +/- standard deviation), increased in the modified-implement training group by 4.0 km x h(-1) and in the traditional training group by 1.3 km x h(-1) (difference, 2.7 km x h(-1); 90% confidence limits, 1.2 to 4.2 km x h(-1)). For a minimum worthwhile change of 5 km x h(-1), the chances that the true effect on bowling speed was practically beneficial/trivial/harmful were 1.0/99/< 0.1%. For bowling accuracy, the chances were 1/48/51%. This modified-implement training programme is not a useful training strategy for club cricketers.     

General Media and Society

Atwan, Robert, Barry Orton, and Alliam Vesterman. American Mass Media: Industries and Issues (New York: Random House, 1978—$8.95, paper)

Dennis, Everette E., Arnold H. Ismach, and Donald M. Gillmor, eds. Enduring Issues in Mass Communication (St. Paul, Minn.: West P.' _fishing Co., 1978—price not given, paper)

White, David Manning, and John Pendleton, eds. Popular Culture: Mirror of American Life (Del Mar, Calif.: Publisher's Inc., 1977—$11.95/6.25)

Charlene J. Brown, Trevor R. Brown, and William L. Rivers' The Media and the People (New York: Holt, Rinehart a Winston, 1978 —price not given, paper)

Malcolm Muggeridge's Christ and the Media (Grand Rapids, Mich.: William B. Eerdmans Publishing Co., 1977— $5.95)     

Teachers’ journeys towards critical use of ICT

Teachers have a central role in developing new learning models in schools. This paper describes a study that explored teachers’ confidence and competence in using Information and Communication Technologies (ICT) as they participated in an ICT development project conducted by an Australian education system in 12 primary schools. The project aimed to develop ICT integrated teaching practices by providing in‐class equipment and teacher professional development in ICT use, curriculum development and teaching strategies. The study used qualitative data from teacher records of Professional Development and Action Learning to identify three of four stages proposed to describe teachers’ ICT learning. Teachers moved from gaining basic ICT skills, to conducting ICT‐focused lessons and eventually appropriate ICT integration. Teachers who progressed to the third stage were able to exploit additional learning opportunities and begin to make fundamental changes to their pedagogy, but needed more time to reach the final stage of challenging existing pedagogical structures.     

Using word embeddings in Twitter election classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).     

Tax incentives for R&D: a critical evaluation

A number of policy proposals have recently been presented that seek to address the problem of declining productivity growth. Many of these are aimed at encouraging private R&D spending through tax incentives. This paper first reviews some of these proposals, then evaluates tax incentives per se as a policy tool for encouraging R&D growth, and finally offers suggestions for redesigning R&D related tax incentive policies.