title: The influence of leaders organizational citizenship behaviors and counterproductive work behaviors on employee wellbeing: A multilevel investigation

authors: Roche, Maree A.

abstract: Leader’s organizational citizenship behaviors (OCBs) have been found to directly and indirectly enhance group OCBs, while counter productive work behaviors (CWBs) can have an effect on employee stress and related problems. While previous research has considered these separately, Moral Licensing Theory suggests that those leaders who engage in positive behaviors can feel that they are then able to subsequently engage in negative behaviors. We test the influence of 134 leaders’ on 306 followers job burnout, and find leaders’ self-rated organizational citizenship behaviors (OCBs) were negatively related to follower-rated leaders’ counterproductive work behaviors (CWBs) and followers’ job burnout, and leaders’ CWB were positively related. We also tested and found that CWB interacted with OCBs, which showed that leaders’ who highly engage in positive behaviors still have a positive influence on their followers even when they engage in negative behaviors. Overall, we find empirical support for moral licensing, finding that leaders’ positive behaviors are still most important.

text: The influence of leaders organizational citizenship behaviors and counterproductive work behaviors on employee wellbeing: A multilevel investigation Leader’s organizational citizenship behaviors (OCBs) have been found to directly and indirectly enhance group OCBs, while counter productive work behaviors (CWBs) can have an effect on employee stress and related problems. While previous research has considered these separately, Moral Licensing Theory suggests that those leaders who engage in positive behaviors can feel that they are then able to subsequently engage in negative behaviors. We test the influence of 134 leaders’ on 306 followers job burnout, and find leaders’ self-rated organizational citizenship behaviors (OCBs) were negatively related to follower-rated leaders’ counterproductive work behaviors (CWBs) and followers’ job burnout, and leaders’ CWB were positively related. We also tested and found that CWB interacted with OCBs, which showed that leaders’ who highly engage in positive behaviors still have a positive influence on their followers even when they engage in negative behaviors. Overall, we find empirical support for moral licensing, finding that leaders’ positive behaviors are still most important.

year: 2015

title: Development of an innovative and efficient urban vegetation monitoring system for sustainable urban ecology

authors: Alighaleh, Pejman

subjects: Urban Vegetation

abstract: In the rapidly urbanizing environment of New Zealand, monitoring and preserving urban green spaces are crucial for ecological balance and sustainability. Urban vegetation significantly contributes to the ecological and social fabric of city life, making its distribution, health, and diversity essential for effective urban management and environmental protection. This research addresses a significant gap in current methodologies for monitoring urban greenery by introducing
a more efficient, accurate, and less labor-intensive approach using deep learning technology.
Extensive data collection, preprocessing, and training were key components of this research. Image collection routes were meticulously planned to ensure a comprehensive dataset representing diverse urban vegetation. A major challenge was the quality and privacy concerns within the dataset. Continuous image capturing in urban areas led to issues like blurry, repetitive, and nonvegetative images. To mitigate these issues, diverse filters were introduced to enhance the dataset's quality and ensure privacy protection. Focusing on advanced computer vision systems, this thesis employed the YOLOv5 and YOLOv8 models for automated urban vegetation detection using RGB images from car-mounted cameras. The models were chosen for their effectiveness in image recognition, with various image augmentation techniques and annotation tools enriching the training process. The thesis also explored transfer learning and layer freezing techniques in model training, along with a unique method of using larger bounding boxes for tree annotations to enhance the detection of specific tree species.
The models' performance in vegetation detection was evaluated through precision and recall rates, highlighting their reliability and efficiency, with YOLOv5x demonstrating the highest precision (0.93) and recall (0.90) for urban vegetation detection. In tree species detection, the YOLOv5m with Transfer Learning model achieved a strong balance of precision (0.95), recall (0.77), and accuracy (0.86), while YOLOv8m with Transfer Learning excelled in recall (0.93), proving useful for applications requiring high true positive identification. For specific tree detection, the YOLOv8m_Bigger_size model led with high precision (0.95), recall (0.98), and the top mAP50-95 score (0.79) in validation. These results underscore the models' applicability in diverse urban scenarios and reinforce the feasibility of this approach for large-scale urban vegetation monitoring. The research findings indicate that deep learning technology, particularly the YOLOv5m and YOLOv8m models, significantly enhances the efficiency and accuracy of urban vegetation monitoring. The innovative use of car-mounted cameras for data collection marks
a significant advancement over traditional methods, enabling the capture of large-scale urban vegetation data with minimal intrusion and high efficiency.
In summary, this study demonstrates that applying advanced computer vision systems can markedly improve urban vegetation monitoring. The integration of YOLOv5 and YOLOv8 models in New Zealand's urban landscapes offers a novel approach to address the challenges of urbanization. The models are adaptable for use in other New Zealand cities with access to similar urban vegetation datasheets, crucial for evaluating model accuracy and enhancing precision in future models. This thesis advances our understanding of urban vegetation monitoring using computer vision and highlights the potential of deep learning models in enhancing environmental conservation efforts. The insights and methodologies presented provide valuable tools for urban environmental management and suggest new directions for future research and applications in this critical area.

text: Development of an innovative and efficient urban vegetation monitoring system for sustainable urban ecology In the rapidly urbanizing environment of New Zealand, monitoring and preserving urban green spaces are crucial for ecological balance and sustainability. Urban vegetation significantly contributes to the ecological and social fabric of city life, making its distribution, health, and diversity essential for effective urban management and environmental protection. This research addresses a significant gap in current methodologies for monitoring urban greenery by introducing
a more efficient, accurate, and less labor-intensive approach using deep learning technology.
Extensive data collection, preprocessing, and training were key components of this research. Image collection routes were meticulously planned to ensure a comprehensive dataset representing diverse urban vegetation. A major challenge was the quality and privacy concerns within the dataset. Continuous image capturing in urban areas led to issues like blurry, repetitive, and nonvegetative images. To mitigate these issues, diverse filters were introduced to enhance the dataset's quality and ensure privacy protection. Focusing on advanced computer vision systems, this thesis employed the YOLOv5 and YOLOv8 models for automated urban vegetation detection using RGB images from car-mounted cameras. The models were chosen for their effectiveness in image recognition, with various image augmentation techniques and annotation tools enriching the training process. The thesis also explored transfer learning and layer freezing techniques in model training, along with a unique method of using larger bounding boxes for tree annotations to enhance the detection of specific tree species.
The models' performance in vegetation detection was evaluated through precision and recall rates, highlighting their reliability and efficiency, with YOLOv5x demonstrating the highest precision (0.93) and recall (0.90) for urban vegetation detection. In tree species detection, the YOLOv5m with Transfer Learning model achieved a strong balance of precision (0.95), recall (0.77), and accuracy (0.86), while YOLOv8m with Transfer Learning excelled in recall (0.93), proving useful for applications requiring high true positive identification. For specific tree detection, the YOLOv8m_Bigger_size model led with high precision (0.95), recall (0.98), and the top mAP50-95 score (0.79) in validation. These results underscore the models' applicability in diverse urban scenarios and reinforce the feasibility of this approach for large-scale urban vegetation monitoring. The research findings indicate that deep learning technology, particularly the YOLOv5m and YOLOv8m models, significantly enhances the efficiency and accuracy of urban vegetation monitoring. The innovative use of car-mounted cameras for data collection marks
a significant advancement over traditional methods, enabling the capture of large-scale urban vegetation data with minimal intrusion and high efficiency.
In summary, this study demonstrates that applying advanced computer vision systems can markedly improve urban vegetation monitoring. The integration of YOLOv5 and YOLOv8 models in New Zealand's urban landscapes offers a novel approach to address the challenges of urbanization. The models are adaptable for use in other New Zealand cities with access to similar urban vegetation datasheets, crucial for evaluating model accuracy and enhancing precision in future models. This thesis advances our understanding of urban vegetation monitoring using computer vision and highlights the potential of deep learning models in enhancing environmental conservation efforts. The insights and methodologies presented provide valuable tools for urban environmental management and suggest new directions for future research and applications in this critical area.

year: 2024

title: The Oral Transmission of Mātauranga Māori in regard to Traditional Hokianga Stories and the Revitalization of Significant Place Names.

authors: Barnes, Maria

subjects: Hokianga

abstract: This thesis researched the oral transmission of mātauranga Māori from one generation to another with a particular focus on the significance of place names in the Hokianga area from Waimamaku through to Opononi. It also sought to understand the stories and history that accompany this area. Our oral traditions are critical in helping to define our identity as Māori, and our relationship with the land.

This thesis begins by looking at the ways in which we as Māori are connected to the land and some of our traditional values regarding cultural practices and the way our tūpuna lived.  It also shares an example of the way in which our oral traditions and histories were preserved and taught.  There is then a detailed section regarding the significance of Hokianga place names within the Waimamaku, Omapere and Opononi area of Hokianga.  This information has been provided orally from the generosity of kaumātua of our area.  Following this I have concluded by providing an overall statement about the significance of findings and outcomes of my research.

This kaupapa is important as more and more of these place names and their histories will continue to be lost if records are not kept and shared with future generations.  I also understand the importance of protecting this knowledge.  Through colonisation and over time the use of our traditional educational practices and our place names and their histories have been forgotten, watered down, and sometimes lost.  Our learning methods, histories and whakapapa constitute important elements of who we are as Māori. They recognise the past, help us to understand today and guide us into the future. 

The research strategy used in my thesis is collaborative storying co-constructed between myself the researcher and local kaumātua deemed to hold traditional knowledge of our area.  This has provided the opportunity to find out significant place names and our local histories. Prior to working on this thesis only a few names were known and understood.  However in the end we are able to get a more detailed picture of our history.  There are still plenty more place names to be found and understood.


We live and come from a beautiful place.

text: The Oral Transmission of Mātauranga Māori in regard to Traditional Hokianga Stories and the Revitalization of Significant Place Names. This thesis researched the oral transmission of mātauranga Māori from one generation to another with a particular focus on the significance of place names in the Hokianga area from Waimamaku through to Opononi. It also sought to understand the stories and history that accompany this area. Our oral traditions are critical in helping to define our identity as Māori, and our relationship with the land.

This thesis begins by looking at the ways in which we as Māori are connected to the land and some of our traditional values regarding cultural practices and the way our tūpuna lived.  It also shares an example of the way in which our oral traditions and histories were preserved and taught.  There is then a detailed section regarding the significance of Hokianga place names within the Waimamaku, Omapere and Opononi area of Hokianga.  This information has been provided orally from the generosity of kaumātua of our area.  Following this I have concluded by providing an overall statement about the significance of findings and outcomes of my research.

This kaupapa is important as more and more of these place names and their histories will continue to be lost if records are not kept and shared with future generations.  I also understand the importance of protecting this knowledge.  Through colonisation and over time the use of our traditional educational practices and our place names and their histories have been forgotten, watered down, and sometimes lost.  Our learning methods, histories and whakapapa constitute important elements of who we are as Māori. They recognise the past, help us to understand today and guide us into the future. 

The research strategy used in my thesis is collaborative storying co-constructed between myself the researcher and local kaumātua deemed to hold traditional knowledge of our area.  This has provided the opportunity to find out significant place names and our local histories. Prior to working on this thesis only a few names were known and understood.  However in the end we are able to get a more detailed picture of our history.  There are still plenty more place names to be found and understood.


We live and come from a beautiful place.

year: 2010