title: "Not right in the head": How should teachers assess new talk about teenagers?

authors: Payne, Monica A.

subjects: adolescent discourse

abstract: Recently in New Zealand the Prime Minister's Chief Science Advisor has warned of changing circumstances creating a "powder keg" during adolescence, another top government advisor is reported as claiming young people's behaviour problems are the country's "biggest social issue", and the catchphrase of a parenting series on national television has been that teenagers are best understood as "not right in the head". Perhaps it is unsurprising that surveys have been reporting high levels of teacher stress and increasing levels of abuse and assault. Should secondary teaching therefore be considered a dubious career choice and a mass exodus from the profession anticipated? With regard to the implications for those whose lives continue to meet and mix in schools, this paper critically examines some of the local and overseas "expert talk" inspired by key features of scientific assertions regarding the changing nature of physical and cognitive development in adolescence.

text: "Not right in the head": How should teachers assess new talk about teenagers? Recently in New Zealand the Prime Minister's Chief Science Advisor has warned of changing circumstances creating a "powder keg" during adolescence, another top government advisor is reported as claiming young people's behaviour problems are the country's "biggest social issue", and the catchphrase of a parenting series on national television has been that teenagers are best understood as "not right in the head". Perhaps it is unsurprising that surveys have been reporting high levels of teacher stress and increasing levels of abuse and assault. Should secondary teaching therefore be considered a dubious career choice and a mass exodus from the profession anticipated? With regard to the implications for those whose lives continue to meet and mix in schools, this paper critically examines some of the local and overseas "expert talk" inspired by key features of scientific assertions regarding the changing nature of physical and cognitive development in adolescence.

year: 2011

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: What does 'Ethical Research'' look like? An indigenous perspective on big data & precision medicine

authors: Hudson, Maui

abstract: Southeast Native American Health Data (SENAHD) in partnership with Native BioData Consortium will host an online symposium about the ethical use of Indigenous genetic, environmental, biological, and cultural data. SENAHD is based at UNC Chapel Hill. Our partner, Native BioData Consortium is located on the Cheyanne River Sioux Reservation in South Dakota. NativeBio is one of the most important voices advocating for Indigenous health equity, data sovereignty, and ethical research practices. NativeBio is coming to the Southeast to recognize and include our region in important conversations taking place across Indian Country

text: What does 'Ethical Research'' look like? An indigenous perspective on big data & precision medicine Southeast Native American Health Data (SENAHD) in partnership with Native BioData Consortium will host an online symposium about the ethical use of Indigenous genetic, environmental, biological, and cultural data. SENAHD is based at UNC Chapel Hill. Our partner, Native BioData Consortium is located on the Cheyanne River Sioux Reservation in South Dakota. NativeBio is one of the most important voices advocating for Indigenous health equity, data sovereignty, and ethical research practices. NativeBio is coming to the Southeast to recognize and include our region in important conversations taking place across Indian Country

year: 2022