title: Making our work matter: From spectator to engagement through public organization and management studies

authors: Cunliffe, Ann L.

abstract: There are growing concerns about making our work matter in society and organizations, narrowing the theory–practice gap by doing research that has impact. In this paper, we suggest that there is more at stake than the issue of relating theory to practice, that we need to consider how we generate knowledge and think about its relevance. We argue that at the heart of such an endeavor lie critical ontological and epistemological considerations: first, the need to rethink our self–Other relationships—the nature of the relationship between ourselves as scholars and community members; and second, the need to generate more situated and relational forms of knowing-from-within that address social and environmental issues. Drawing on the work of critical sociologist Michael Burawoy, we elaborate four approaches to generating knowledge within organization and management studies (OMS), arguing that a public organization and management studies approach offers one way of making our work matter, requiring us to move from being spectators of the world to becoming actively engaged with multiple Others in generating knowledge and action. We discuss both the challenges and opportunities of a public OMS, offering examples of how we can become more actively engaged.

text: Making our work matter: From spectator to engagement through public organization and management studies There are growing concerns about making our work matter in society and organizations, narrowing the theory–practice gap by doing research that has impact. In this paper, we suggest that there is more at stake than the issue of relating theory to practice, that we need to consider how we generate knowledge and think about its relevance. We argue that at the heart of such an endeavor lie critical ontological and epistemological considerations: first, the need to rethink our self–Other relationships—the nature of the relationship between ourselves as scholars and community members; and second, the need to generate more situated and relational forms of knowing-from-within that address social and environmental issues. Drawing on the work of critical sociologist Michael Burawoy, we elaborate four approaches to generating knowledge within organization and management studies (OMS), arguing that a public organization and management studies approach offers one way of making our work matter, requiring us to move from being spectators of the world to becoming actively engaged with multiple Others in generating knowledge and action. We discuss both the challenges and opportunities of a public OMS, offering examples of how we can become more actively engaged.

year: 2022

title: Machine Learning for Adaptive Computer Game Opponents

authors: Miles, Jonathan David

subjects: Machine Learning

abstract: This thesis investigates the use of machine learning techniques in 
computer games to create a computer player that adapts to its opponent's 
game-play. This includes first confirming that machine learning 
algorithms can be integrated into a modern computer game without have a 
detrimental effect on game performance, then experimenting with 
different machine learning techniques to maximize the computer player's 
performance. Experiments use three machine learning techniques; static 
prediction models, continuous learning, and reinforcement learning. 
Static models show the highest initial performance but are not able to 
beat a simple opponent. Continuous learning is able to improve the 
performance achieved with static models but the rate of improvement 
drops over time and the computer player is still unable to beat the 
opponent. Reinforcement learning methods have the highest rate of 
improvement but the lowest initial performance. This limits the 
effectiveness of reinforcement learning because a large number of 
episodes are required before performance becomes sufficient to match the 
opponent.

text: Machine Learning for Adaptive Computer Game Opponents This thesis investigates the use of machine learning techniques in 
computer games to create a computer player that adapts to its opponent's 
game-play. This includes first confirming that machine learning 
algorithms can be integrated into a modern computer game without have a 
detrimental effect on game performance, then experimenting with 
different machine learning techniques to maximize the computer player's 
performance. Experiments use three machine learning techniques; static 
prediction models, continuous learning, and reinforcement learning. 
Static models show the highest initial performance but are not able to 
beat a simple opponent. Continuous learning is able to improve the 
performance achieved with static models but the rate of improvement 
drops over time and the computer player is still unable to beat the 
opponent. Reinforcement learning methods have the highest rate of 
improvement but the lowest initial performance. This limits the 
effectiveness of reinforcement learning because a large number of 
episodes are required before performance becomes sufficient to match the 
opponent.

year: 2009

title: ‘Hospitality’, boundary crossing and thresholds applied in education: Embodying manaakitanga and whanaungatanga

authors: Wright, Noeline

abstract: In everyday understanding, ‘hospitality’ refers to paid work contexts; commodities and transactional tasks. Hospitality can, however, have much broader significance. It can be understood in cultural and social terms within and beyond transactional contexts. In Māori cultural knowledge and practices for example, hospitality has reciprocal, relational nuances. Perhaps both views imply that ‘hospitality’ is an act of ‘crossing boundaries […] or thresholds’ as Still (2013: 4) suggested. Relational, reciprocal, boundary-crossing practices may also infer ritual understandings of respect, kindness, generosity and harmony. Crossing thresholds is a central concept in Māori knowledge and practices and is central to this article exploring concepts and practices of hospitality in a new primary school focused on building its practices on relational and reciprocal values. In Aotearoa New Zealand educational contexts, hospitality is a cultural, social reciprocal and relational practice. Thus, through examining ‘hospitality’, ‘borders’ and ‘thresholds’ across the boundaries of education and commerce, we hope to illuminate both connections and differences. We do so through reviewing both literature about ‘hospitality’, ‘borders’ and ‘thresholds’ and interview data from a new school intentionally valuing whanaungatanga.

text: ‘Hospitality’, boundary crossing and thresholds applied in education: Embodying manaakitanga and whanaungatanga In everyday understanding, ‘hospitality’ refers to paid work contexts; commodities and transactional tasks. Hospitality can, however, have much broader significance. It can be understood in cultural and social terms within and beyond transactional contexts. In Māori cultural knowledge and practices for example, hospitality has reciprocal, relational nuances. Perhaps both views imply that ‘hospitality’ is an act of ‘crossing boundaries […] or thresholds’ as Still (2013: 4) suggested. Relational, reciprocal, boundary-crossing practices may also infer ritual understandings of respect, kindness, generosity and harmony. Crossing thresholds is a central concept in Māori knowledge and practices and is central to this article exploring concepts and practices of hospitality in a new primary school focused on building its practices on relational and reciprocal values. In Aotearoa New Zealand educational contexts, hospitality is a cultural, social reciprocal and relational practice. Thus, through examining ‘hospitality’, ‘borders’ and ‘thresholds’ across the boundaries of education and commerce, we hope to illuminate both connections and differences. We do so through reviewing both literature about ‘hospitality’, ‘borders’ and ‘thresholds’ and interview data from a new school intentionally valuing whanaungatanga.

year: 2021-03-01