Adam Poulsen is the Acting Editor at Countryman. He covers a range of topics including livestock, court news, and general news, with a focus on farm management, animal rights, renewable energy, sustainable agriculture, and corporate fraud. His work has been featured in numerous publications, including the Harvey-Waroona Reporter, North West Telegraph, and The West Australian.
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Merredin, Australia (Local)
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Adam Poulsen's coverage revolves around local news with a focus on agriculture, government, and business in Western Australia. He seems to be interested in stories related to agricultural innovations, awards received by farmers or companies, as well as government announcements impacting the agricultural sector.
To effectively pitch to Adam, consider providing unique insights into local farming practices or innovations that are positively impacting the industry in Western Australia. Additionally, if you have information about government policies affecting agriculture in this region or noteworthy achievements by local businesses within the agricultural sector, it could be of interest to him.
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By: Adam Poulsen, Ian B Hickie, Frank Iorfino, Mathew Varidel, Carla Gorban, Elizabeth M Scott, Sarah McKenna, Melissa Aji, William Capon, Ashlee Turner, Jacob J Crouse
Abstract Background: Mental disorders are the leading cause of disability in young people (aged 12‐30 years), and their incidence constitutes a major health crisis.
By: Ian B Hickie, Adam Poulsen, Mafruha Alam
By: Roger A. Søraa, Scarlet Tunney, Bart Custers, Adam Poulsen, Tessa Verhoef, Anne Noel
Gender classification systems (GCSs) on social media platforms, such as X (formerly Twitter), infer users' gender for targeted advertising and personalization. However, these systems rely on binary classifications that fail to capture gender diversity, often leading to misclassification (i.e. misgendering). This study is a comprehensive analysis of algorithmic misgendering and its impact on user perceptions through a large-scale online global survey (N = 1523). In the first stage, we assess the prevalence of misgendering on X by analyzing the accuracy of inferred gender classifications. Our findings reveal that women and LGBTQ+ users are disproportionately misclassified, which highlights structural biases in gender inference systems. In the second stage, given that the emotional and social consequences of algorithmic gender inference remain underexplored, we examine how users perceive and respond to misgendering. Using ordinal logistic regression models, we find that individuals who experience misgendering report significantly more aversion to X's gender policies. Furthermore, increased platform engagement is linked to stronger opinions on gender inference, reducing neutrality toward these systems. Beyond these findings, our study also reveals how opaque gender classification is, as many users struggle to locate, understand, or challenge their inferred gender within X's interface. This lack of transparency raises concerns about agency, algorithmic literacy, data protection, and fairness. Based on our work, we suggest regulatory measures to ensure greater transparency and user control over gender classification, contributing to ongoing debates on algorithmic discrimination and inclusive AI governance on current and future social media platforms.
