title: Professional learning places and spaces: the staffroom as a site of beginning teacher induction and transition

authors: lisahunter

subjects: beginning teachers

abstract: This paper argues that the staffroom is an important professional learning space where beginning teachers interact to understand who they are and the nature of their professional work. The authors highlight the theoretical importance of space and place in the construction and negotiation of beginning teacher subjectivities. To illustrate the staffroom as a particular place where important professional learning could occur the authors use two narratives based on the lived experiences of two beginning teachers, one in a primary context, the other secondary. The authors conclude by calling for greater research attention to the significance of the staffroom and its interaction with teacher subjectivities. At the level of practice we also call for the teaching profession to recognise staffrooms as important sites of professional learning and places that should support induction and mentoring of beginning teachers. Such recognition could enhance the retention, satisfaction, and effectiveness of new and experienced teachers alike.

text: Professional learning places and spaces: the staffroom as a site of beginning teacher induction and transition This paper argues that the staffroom is an important professional learning space where beginning teachers interact to understand who they are and the nature of their professional work. The authors highlight the theoretical importance of space and place in the construction and negotiation of beginning teacher subjectivities. To illustrate the staffroom as a particular place where important professional learning could occur the authors use two narratives based on the lived experiences of two beginning teachers, one in a primary context, the other secondary. The authors conclude by calling for greater research attention to the significance of the staffroom and its interaction with teacher subjectivities. At the level of practice we also call for the teaching profession to recognise staffrooms as important sites of professional learning and places that should support induction and mentoring of beginning teachers. Such recognition could enhance the retention, satisfaction, and effectiveness of new and experienced teachers alike.

year: 2011

title: Naive Bayes for regression

authors: Frank, Eibe

subjects: Science & Technology

abstract: Despite its simplicity, the naive Bayes learning scheme performs well on most classification tasks, and is often significantly more accurate than more sophisticated methods. Although the probability estimates that it produces can be inaccurate, it often assigns maximum probability to the correct class. This suggests that its good performance might be restricted to situations where the output is categorical. It is therefore interesting to see how it performs in domains where the predicted value is numeric, because in this case, predictions are more sensitive to inaccurate probability estimates.
This paper shows how to apply the naive Bayes methodology to numeric prediction (i.e., regression) tasks by modeling the probability distribution of the target value with kernel density estimators, and compares it to linear regression, locally weighted linear regression, and a method that produces “model trees”—decision trees with linear regression functions at the leaves. Although we exhibit an artificial dataset for which naive Bayes is the method of choice, on real-world datasets it is almost uniformly worse than locally weighted linear regression and model trees. The comparison with linear regression depends on the error measure: for one measure naive Bayes performs similarly, while for another it is worse. We also show that standard naive Bayes applied to regression problems by discretizing the target value performs similarly badly.We then present empirical evidence that isolates naive Bayes’ independence assumption as the culprit for its poor performance in the regression setting. These results indicate that the simplistic statistical assumption that naive Bayes makes is indeed more restrictive for regression than for classification.

text: Naive Bayes for regression Despite its simplicity, the naive Bayes learning scheme performs well on most classification tasks, and is often significantly more accurate than more sophisticated methods. Although the probability estimates that it produces can be inaccurate, it often assigns maximum probability to the correct class. This suggests that its good performance might be restricted to situations where the output is categorical. It is therefore interesting to see how it performs in domains where the predicted value is numeric, because in this case, predictions are more sensitive to inaccurate probability estimates.
This paper shows how to apply the naive Bayes methodology to numeric prediction (i.e., regression) tasks by modeling the probability distribution of the target value with kernel density estimators, and compares it to linear regression, locally weighted linear regression, and a method that produces “model trees”—decision trees with linear regression functions at the leaves. Although we exhibit an artificial dataset for which naive Bayes is the method of choice, on real-world datasets it is almost uniformly worse than locally weighted linear regression and model trees. The comparison with linear regression depends on the error measure: for one measure naive Bayes performs similarly, while for another it is worse. We also show that standard naive Bayes applied to regression problems by discretizing the target value performs similarly badly.We then present empirical evidence that isolates naive Bayes’ independence assumption as the culprit for its poor performance in the regression setting. These results indicate that the simplistic statistical assumption that naive Bayes makes is indeed more restrictive for regression than for classification.

year: 2000-10-01

title: Maori internal and international migration at the turn of the century: An Australasian perspective

authors: Bedford, Richard

abstract: At the beginning of the twenty-first century there were two major national clusters of Maori: New Zealand, the ancestral home for Maori, and Australia, home to a much smaller Maori population from the early years of the nineteenth century. In the 2001 censuses of New Zealand and Australia, the usually resident Maori populations were, respectively, 526,281 (ethnic group classification) and 72,956 (ancestry classification). In this paper we examine four dimensions of Maori population movement between 1996 and 2001 using the census data from New Zealand and Australia: 1) internal migration between rural and urban areas in New Zealand; 2) internal migration between rural and urban areas in Australia; 3) migration into New Zealand of Maori resident overseas in 1996; 4) migration into Australia of Maori resident overseas in 1996. There has never been a comprehensive assessment of Maori migration in an Australasian context before, but in the light of developments in population exchanges between New Zealand and Australia this sort of analysis is critical if one wishes to understand contemporary Maori population dynamics.

text: Maori internal and international migration at the turn of the century: An Australasian perspective At the beginning of the twenty-first century there were two major national clusters of Maori: New Zealand, the ancestral home for Maori, and Australia, home to a much smaller Maori population from the early years of the nineteenth century. In the 2001 censuses of New Zealand and Australia, the usually resident Maori populations were, respectively, 526,281 (ethnic group classification) and 72,956 (ancestry classification). In this paper we examine four dimensions of Maori population movement between 1996 and 2001 using the census data from New Zealand and Australia: 1) internal migration between rural and urban areas in New Zealand; 2) internal migration between rural and urban areas in Australia; 3) migration into New Zealand of Maori resident overseas in 1996; 4) migration into Australia of Maori resident overseas in 1996. There has never been a comprehensive assessment of Maori migration in an Australasian context before, but in the light of developments in population exchanges between New Zealand and Australia this sort of analysis is critical if one wishes to understand contemporary Maori population dynamics.

year: 2004