title: Te ao hurihuri : iwi identification in the census

authors: Kukutai, Tahu

abstract: As the flagship of official statistics, the national population census is an important forum for the recognition of indigenous peoples in their homelands and territories (United Nations, 2008). While most governments worldwide count and classify their populations by ethnicity, New Zealand is rare in enabling multiple expressions of indigenous identity in the census. Since 1991, it has been possible to identify as Māori in three ways: by descent, ethnicity and iwi (tribal) affiliation (see Figure 1). The Māori descent population is the largest— in 2013, just under 669,000 individuals ticked the Māori descent box. The number identifying as Māori by ethnicity - intended as a measure of cultural belonging - was substantially lower at just under 600,000. Most of the remaining 69,000 Māori descendants identified solely as NZ European. In 2013 nearly 83 per cent of Māori descendants (n=535,941) reported belonging to at least one iwi. Of those that did not report an iwi affiliation, 40 per cent did not identify as Māori by ethnicity either.

text: Te ao hurihuri : iwi identification in the census As the flagship of official statistics, the national population census is an important forum for the recognition of indigenous peoples in their homelands and territories (United Nations, 2008). While most governments worldwide count and classify their populations by ethnicity, New Zealand is rare in enabling multiple expressions of indigenous identity in the census. Since 1991, it has been possible to identify as Māori in three ways: by descent, ethnicity and iwi (tribal) affiliation (see Figure 1). The Māori descent population is the largest— in 2013, just under 669,000 individuals ticked the Māori descent box. The number identifying as Māori by ethnicity - intended as a measure of cultural belonging - was substantially lower at just under 600,000. Most of the remaining 69,000 Māori descendants identified solely as NZ European. In 2013 nearly 83 per cent of Māori descendants (n=535,941) reported belonging to at least one iwi. Of those that did not report an iwi affiliation, 40 per cent did not identify as Māori by ethnicity either.

year: 2015

title: Research note: Empirical analysis of ethnic pay gaps in New Zealand

authors: Cochrane, William

abstract: This research note explores the factors contributing to ethnic pay gaps in New Zealand. The emphasis here is on description, as a causal analysis is beyond the capability of the methods used. The gaps between the average (as well as median) hourly wages for the European workforce relative to Māori and Pacific workers are substantial. A statistical analysis by Treasury in 2018 also showed that the ratio in average hourly wages (based on published survey estimates by Stats NZ) for both ethnic groups relative to European had stayed at a similar level for the last decade; as they state “there has been movement from year to year but no consistent upward or downward trend” (Treasury, 2018, p.1).

text: Research note: Empirical analysis of ethnic pay gaps in New Zealand This research note explores the factors contributing to ethnic pay gaps in New Zealand. The emphasis here is on description, as a causal analysis is beyond the capability of the methods used. The gaps between the average (as well as median) hourly wages for the European workforce relative to Māori and Pacific workers are substantial. A statistical analysis by Treasury in 2018 also showed that the ratio in average hourly wages (based on published survey estimates by Stats NZ) for both ethnic groups relative to European had stayed at a similar level for the last decade; as they state “there has been movement from year to year but no consistent upward or downward trend” (Treasury, 2018, p.1).

year: 2023-10-19

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