title: Sampling community discourses as a method for assessing "public opinion"

authors: Guerin, Bernard

subjects: racial prejudice

abstract: In working with the topics of racial discrimination and household energy reductions it is clear that just measuring attitudes is no longer sufficient. We need a fuller social context to understand what is said about these topics, as the same words can be used for many reasons. I am working with four new methods for "sampling community discourses" on topics such as racial prejudice, to replace or supplement the measurement of attitudes. The initial analysis is whether the talk functions to get someone to do something directly, to get someone to believe the 'facts', or whether the talk functions for the establishment or maintenance of social relationships. Many problems remain, especially the representativeness of the sampling and keeping as much of the context of the talk as possible.

text: Sampling community discourses as a method for assessing "public opinion" In working with the topics of racial discrimination and household energy reductions it is clear that just measuring attitudes is no longer sufficient. We need a fuller social context to understand what is said about these topics, as the same words can be used for many reasons. I am working with four new methods for "sampling community discourses" on topics such as racial prejudice, to replace or supplement the measurement of attitudes. The initial analysis is whether the talk functions to get someone to do something directly, to get someone to believe the 'facts', or whether the talk functions for the establishment or maintenance of social relationships. Many problems remain, especially the representativeness of the sampling and keeping as much of the context of the talk as possible.

year: 2004-09-01

title: "More Societal than Generational": Examining the Construction and Resistance of Generational Messages in the Workplace

authors: Hitchcock, Steven David

subjects: Millennials

abstract: The Millennial generation, those born between 1980-2000, have drawn vast, sometimes fanatical, criticism in popular media. Slated as narcissistic praise hounds, they are cast as demanding graduate divas who are about to attack the workplace and everything ‘you hold sacred’ (Clark, 2008; Safer, 2007). The abundance of such messages about this generation in formats ‘tailored, targeted, and consumed’ by the public is problematic given that generational constructs are by many perceived as sacrosanct (Myers et al, 2010). 

The proliferation of such criticism is by no means innocuous given the very likely impact that they will have on Millennial work opportunities. For many scholars the field of Millennial research suffers from a lack of empirical and cross sectional data to establish more calculated and careful generational constructs, – instead relying on or reacting to popular negative stereotypes. While some Millennial scholarship has begun to move beyond criticisms of popular media, Millennial research is by many considered contradictory at best and confusing at worst (Kowske et al, 2010). Additional difficulties arise when the scramble to publish more research-based work has led to methodologies which are inherently flawed because they reinforce the very same monolithic generational categories they are supposed to assess.

This study, undertaken in New Zealand, explores critical approaches as a means of examining the construction of generational messages and the establishment of generational difference. As a starting point, this small-scale examination analyses the very way in which generational messages are constructed and resisted within the workplace through an analysis of interviews undertaken with 26 employees of a Small to Medium Enterprise (SME) in the information technology sector.

Unlike many generational studies, this project did not seek to draw conclusions by framing differences and measuring responses across generational lines, but rather took a bottom-up approach to understand how participants themselves constructed and resisted messages about generational difference. The project asked two research questions: First, how are generational messages constructed in the context of the workplace? And second, how are generational messages resisted in the workplace? Through axial coding this research categorized five themes under which participants constructed generational difference. These five themes are Technology, Voice, Fairness, Informality, and Stimulus. Broadly speaking, these themes were underpinned by a belief that Millennials have a great demand for respect, democratic process, and the reduction of power distances.

Given the critical approach, the study also observed resistance as a component of the discursive process. As such this research outlines the partiality of resistance and outlines strategies of resistance employed by employees. In line with the idea that construction and resistance are mutually implicated as negotiation, participants were frequently observed simultaneously constructing and resisting generational difference, both synchronically and diachronically. Through axial coding this study also categorized three strategies of resistance. These three strategies are established as Dismissal, the Third Person Effect, and the Decline Metaphor.

This research highlights the usefulness of adopting critical approaches by illustrating the way in which generational meaning is perpetually produced, reproduced, negotiated, and resisted by participants (Murphy, 1998). While there are several factors which are indicative of the Millennial generation, this thesis establishes the hegemonic character of most constructions of generational difference. Given the fragmented and complex state of society, this thesis posits that the usefulness of the monolithic birth-cohort generation has long since passed and we should instead look to understanding generations in terms of their consumption of similar cultural capital.

text: "More Societal than Generational": Examining the Construction and Resistance of Generational Messages in the Workplace The Millennial generation, those born between 1980-2000, have drawn vast, sometimes fanatical, criticism in popular media. Slated as narcissistic praise hounds, they are cast as demanding graduate divas who are about to attack the workplace and everything ‘you hold sacred’ (Clark, 2008; Safer, 2007). The abundance of such messages about this generation in formats ‘tailored, targeted, and consumed’ by the public is problematic given that generational constructs are by many perceived as sacrosanct (Myers et al, 2010). 

The proliferation of such criticism is by no means innocuous given the very likely impact that they will have on Millennial work opportunities. For many scholars the field of Millennial research suffers from a lack of empirical and cross sectional data to establish more calculated and careful generational constructs, – instead relying on or reacting to popular negative stereotypes. While some Millennial scholarship has begun to move beyond criticisms of popular media, Millennial research is by many considered contradictory at best and confusing at worst (Kowske et al, 2010). Additional difficulties arise when the scramble to publish more research-based work has led to methodologies which are inherently flawed because they reinforce the very same monolithic generational categories they are supposed to assess.

This study, undertaken in New Zealand, explores critical approaches as a means of examining the construction of generational messages and the establishment of generational difference. As a starting point, this small-scale examination analyses the very way in which generational messages are constructed and resisted within the workplace through an analysis of interviews undertaken with 26 employees of a Small to Medium Enterprise (SME) in the information technology sector.

Unlike many generational studies, this project did not seek to draw conclusions by framing differences and measuring responses across generational lines, but rather took a bottom-up approach to understand how participants themselves constructed and resisted messages about generational difference. The project asked two research questions: First, how are generational messages constructed in the context of the workplace? And second, how are generational messages resisted in the workplace? Through axial coding this research categorized five themes under which participants constructed generational difference. These five themes are Technology, Voice, Fairness, Informality, and Stimulus. Broadly speaking, these themes were underpinned by a belief that Millennials have a great demand for respect, democratic process, and the reduction of power distances.

Given the critical approach, the study also observed resistance as a component of the discursive process. As such this research outlines the partiality of resistance and outlines strategies of resistance employed by employees. In line with the idea that construction and resistance are mutually implicated as negotiation, participants were frequently observed simultaneously constructing and resisting generational difference, both synchronically and diachronically. Through axial coding this study also categorized three strategies of resistance. These three strategies are established as Dismissal, the Third Person Effect, and the Decline Metaphor.

This research highlights the usefulness of adopting critical approaches by illustrating the way in which generational meaning is perpetually produced, reproduced, negotiated, and resisted by participants (Murphy, 1998). While there are several factors which are indicative of the Millennial generation, this thesis establishes the hegemonic character of most constructions of generational difference. Given the fragmented and complex state of society, this thesis posits that the usefulness of the monolithic birth-cohort generation has long since passed and we should instead look to understanding generations in terms of their consumption of similar cultural capital.

year: 2011

title: Data and model bias in artificial intelligence for healthcare applications in New Zealand

authors: Yogarajan, Vithya

abstract: Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

text: Data and model bias in artificial intelligence for healthcare applications in New Zealand Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

year: 2022-12-22