title: Diversity in Aotearoa New Zealand picturebooks published between 2021 and 2022

authors: Daly, Nicola

subjects: Design

abstract: It is widely accepted that diversity in the literature read by and with children is of great importance, both for ensuring all children see themselves in the stories they share and in ensuring children are aware of lives and experiences outside their own. There is a growing body of international literature critically exploring the diversity present in English-language children’s literature, but to date very little analysing diversity in Aotearoa New Zealand. This study examines 90 picturebooks published between 2021 and 2022 in terms of representations of ethnicity, skin colour, gender, disability, family structures, and language use. Findings indicate similar representations of gender, disability, and family structure to those found internationally but higher representation in relation to the ethnicity and skin colour of primary and secondary characters. New findings in relation to language use are presented. Future research concerning the details of representation in picturebooks and other formats of children’s literature in Aotearoa New Zealand is called for.

text: Diversity in Aotearoa New Zealand picturebooks published between 2021 and 2022 It is widely accepted that diversity in the literature read by and with children is of great importance, both for ensuring all children see themselves in the stories they share and in ensuring children are aware of lives and experiences outside their own. There is a growing body of international literature critically exploring the diversity present in English-language children’s literature, but to date very little analysing diversity in Aotearoa New Zealand. This study examines 90 picturebooks published between 2021 and 2022 in terms of representations of ethnicity, skin colour, gender, disability, family structures, and language use. Findings indicate similar representations of gender, disability, and family structure to those found internationally but higher representation in relation to the ethnicity and skin colour of primary and secondary characters. New findings in relation to language use are presented. Future research concerning the details of representation in picturebooks and other formats of children’s literature in Aotearoa New Zealand is called for.

year: 2025

title: Indigenous people in Aotearoa New Zealand are overrepresented in cannabis convictions

authors: Rapana, Wetini

subjects: Cannabis

abstract: Background: Previous work has demonstrated that cannabis laws have had a disproportionate impact on Māori, the Indigenous people of Aotearoa New Zealand. In 2019, the New Zealand Government amended cannabis laws, providing police with the power to determine whether a therapeutic or health-centred approach would be more beneficial than a conviction. In the current study, we use population level data to assess whether this law change has ameliorated the bias in cannabis convictions for Māori. Methods: Data were drawn from the Integrated Data Infrastructure (IDI), a large government database hosted by Aotearoa New Zealand’s national statistics office. In the IDI, we selected individuals who (1) were between 18 and 65, (2) were Māori or Pākehā (New Zealanders of European descent) and, (3) had any cannabis charges that proceeded to the courts. Results: Māori ethnicity was a significant predictor of the odds of receiving a cannabis conviction for Māori males (Odds: 1.56), with a marginally significant effect for Māori females (Odds: 1.57). Further, for Māori, there was no reduction in the number of cannabis charges before vs. after the amendment to cannabis laws. Conclusion: The current study demonstrates that the bias in cannabis convictions for Māori remain. Given this, the New Zealand Government must follow other countries around the world and move forward on cannabis law reform.

text: Indigenous people in Aotearoa New Zealand are overrepresented in cannabis convictions Background: Previous work has demonstrated that cannabis laws have had a disproportionate impact on Māori, the Indigenous people of Aotearoa New Zealand. In 2019, the New Zealand Government amended cannabis laws, providing police with the power to determine whether a therapeutic or health-centred approach would be more beneficial than a conviction. In the current study, we use population level data to assess whether this law change has ameliorated the bias in cannabis convictions for Māori. Methods: Data were drawn from the Integrated Data Infrastructure (IDI), a large government database hosted by Aotearoa New Zealand’s national statistics office. In the IDI, we selected individuals who (1) were between 18 and 65, (2) were Māori or Pākehā (New Zealanders of European descent) and, (3) had any cannabis charges that proceeded to the courts. Results: Māori ethnicity was a significant predictor of the odds of receiving a cannabis conviction for Māori males (Odds: 1.56), with a marginally significant effect for Māori females (Odds: 1.57). Further, for Māori, there was no reduction in the number of cannabis charges before vs. after the amendment to cannabis laws. Conclusion: The current study demonstrates that the bias in cannabis convictions for Māori remain. Given this, the New Zealand Government must follow other countries around the world and move forward on cannabis law reform.

year: 2022

title: Web crippling design of cold-formed stainless steel channels under interior-two-flange loading condition using deep belief network

authors: Fang, Zhiyuan (Arthur)

subjects: stainless steel

abstract: This research presents a deep-learning framework, namely a deep belief network (DBN), for analyzing the interior-two-flange web crippling performance of cold-formed stainless steel channels with centered and offset web holes. An elastoplastic finite element (FE) model, validated using 101 experimental results which were previously reported in the literature, generates a total of 43,200 data points for training the DBN. When compared to a total of 54 experimental results published in the literature, the DBN predictions were shown to be approximately 10% more conservative. Using the same large training data, the developed DBN model outperformed the Backpropagation Neural Network (a typical shallow artificial neural network) and the PaddlePaddle-based linear regression model. A parametric analysis was then performed using the DBN predictions to explore the effect of section size, web holes and bearing length. Design equations for (reduced) web crippling strength are proposed for the cold-formed stainless steel perforated channels, and the feasibility of the proposed equations was assessed by the conducted reliability analysis.

text: Web crippling design of cold-formed stainless steel channels under interior-two-flange loading condition using deep belief network This research presents a deep-learning framework, namely a deep belief network (DBN), for analyzing the interior-two-flange web crippling performance of cold-formed stainless steel channels with centered and offset web holes. An elastoplastic finite element (FE) model, validated using 101 experimental results which were previously reported in the literature, generates a total of 43,200 data points for training the DBN. When compared to a total of 54 experimental results published in the literature, the DBN predictions were shown to be approximately 10% more conservative. Using the same large training data, the developed DBN model outperformed the Backpropagation Neural Network (a typical shallow artificial neural network) and the PaddlePaddle-based linear regression model. A parametric analysis was then performed using the DBN predictions to explore the effect of section size, web holes and bearing length. Design equations for (reduced) web crippling strength are proposed for the cold-formed stainless steel perforated channels, and the feasibility of the proposed equations was assessed by the conducted reliability analysis.

year: 2023-01