title: Seen but not heard: Women's experiences of educational leadership in Solomon Islands secondary schools

authors: Akao, Shalom Maiasi

subjects: women

abstract: This study is concerned with the representation of women in leadership positions in Solomon Islands. In particular, it explores the educational leadership experiences of women working in Solomon Islands' secondary schools. While much research to date has been carried out in the developed and developing countries, very little has been undertaken in Melanesian countries. As such, we know very little about women's experiences in educational leadership in Melanesia. 
Research data was gathered using qualitative methods. Specifically, interviews were conducted with eight women, five were deputy principals and three were Heads of Departments. All the participants were teaching in schools in an urban centre. Data gathered were analyzed using the thematic analysis approach. The data gathering was conducted in the Solomon Islands in October 2007.
The key findings revealed a number of issues that impacted on the women's leadership in their respective schools. Findings include lack of self confidence, difficulties in balancing work and family, discriminatory attitudes and the influence of the cultural patriarchal norms on the attitudes towards women in leadership positions. Other key findings related to the nature of the organization, in this case, the school. This included a lack of initial preparatory and on - going professional development programmes and a lack of consultation in the decision making process. This study also found that while the women embraced the collaborative approach to leadership, the structural barriers through the hierarchical organization of the schools meant they could not lead the way they would like to.

text: Seen but not heard: Women's experiences of educational leadership in Solomon Islands secondary schools This study is concerned with the representation of women in leadership positions in Solomon Islands. In particular, it explores the educational leadership experiences of women working in Solomon Islands' secondary schools. While much research to date has been carried out in the developed and developing countries, very little has been undertaken in Melanesian countries. As such, we know very little about women's experiences in educational leadership in Melanesia. 
Research data was gathered using qualitative methods. Specifically, interviews were conducted with eight women, five were deputy principals and three were Heads of Departments. All the participants were teaching in schools in an urban centre. Data gathered were analyzed using the thematic analysis approach. The data gathering was conducted in the Solomon Islands in October 2007.
The key findings revealed a number of issues that impacted on the women's leadership in their respective schools. Findings include lack of self confidence, difficulties in balancing work and family, discriminatory attitudes and the influence of the cultural patriarchal norms on the attitudes towards women in leadership positions. Other key findings related to the nature of the organization, in this case, the school. This included a lack of initial preparatory and on - going professional development programmes and a lack of consultation in the decision making process. This study also found that while the women embraced the collaborative approach to leadership, the structural barriers through the hierarchical organization of the schools meant they could not lead the way they would like to.

year: 2008

title: Quantitative mineral mapping of drill core surfaces II: long-wave infrared mineral characterization using μXRF and machine learning

authors: Barker, Rocky D.

subjects: computer science

abstract: Long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine learning approach to predict mineral species and abundances. In this study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analyzed by LWIR and micro-X-ray fluorescence (μXRF). Linear programming-derived mineral abundances from quantified μXRF data were used as training data to construct a series of Random Forest regression models. The LWIR Random Forest models produced mineral proportion estimates with root mean square errors of 1.17 to 6.75% (model predictions) and 1.06 to 6.19% (compared to quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. These results are comparable to the error of proportion estimates from linear spectral deconvolution (±7–15%), a commonly used spectral unmixing technique. Having a mineralogical and chemical training data set makes it possible to identify and quantify mineralogy and provides a more robust and meaningful LWIR spectral interpretation than current methods of utilizing a spectral library or spectral end-member extraction. Using the method presented here, LWIR spectroscopy can be used to overcome the limitations inherent with the use of short-wave infrared (SWIR) in fine-grained, low reflectance rocks. This new approach can be applied to any deposit type, improving the accuracy and speed of infrared data interpretation.

text: Quantitative mineral mapping of drill core surfaces II: long-wave infrared mineral characterization using μXRF and machine learning Long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine learning approach to predict mineral species and abundances. In this study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analyzed by LWIR and micro-X-ray fluorescence (μXRF). Linear programming-derived mineral abundances from quantified μXRF data were used as training data to construct a series of Random Forest regression models. The LWIR Random Forest models produced mineral proportion estimates with root mean square errors of 1.17 to 6.75% (model predictions) and 1.06 to 6.19% (compared to quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. These results are comparable to the error of proportion estimates from linear spectral deconvolution (±7–15%), a commonly used spectral unmixing technique. Having a mineralogical and chemical training data set makes it possible to identify and quantify mineralogy and provides a more robust and meaningful LWIR spectral interpretation than current methods of utilizing a spectral library or spectral end-member extraction. Using the method presented here, LWIR spectroscopy can be used to overcome the limitations inherent with the use of short-wave infrared (SWIR) in fine-grained, low reflectance rocks. This new approach can be applied to any deposit type, improving the accuracy and speed of infrared data interpretation.

year: 2020

title: Ki te kapu o taku ringa - In the palm of my hand [Photography exhibition at Gallagher Academy of Performing Arts]

authors: Hill, Rodrigo

subjects: Mātauranga Māori

abstract: (Exhibition catalogue)
The title Ki te kapu o taku ringa is taken from a line of
‘Maioha mō Waikato’ by Kīngi Tāwhiao. Ejected from their
homelands he and his Waikato brethren took refuge with
their Maniapoto relations in Te Nehenehenui. The maioha
was composed expressing Kīngi Tāwhiao’s love for his
homelands, and his solastalgic holding fast to those lands
‘... in the palm of his hand.’ The words from the song-poem
were used as guide to our research and photography practice.

text: Ki te kapu o taku ringa - In the palm of my hand [Photography exhibition at Gallagher Academy of Performing Arts] (Exhibition catalogue)
The title Ki te kapu o taku ringa is taken from a line of
‘Maioha mō Waikato’ by Kīngi Tāwhiao. Ejected from their
homelands he and his Waikato brethren took refuge with
their Maniapoto relations in Te Nehenehenui. The maioha
was composed expressing Kīngi Tāwhiao’s love for his
homelands, and his solastalgic holding fast to those lands
‘... in the palm of his hand.’ The words from the song-poem
were used as guide to our research and photography practice.

year: 2025