94 research outputs found

    Teacher as learner: a personal reflection on a short course for South African university educators

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    Higher education is understood to play a critical role in ongoing processes of social transformation in post-apartheid South Africa through the production of graduates who are critical and engaged citizens. A key challenge is that institutions of higher education are themselves implicated in reproducing the very hierarchies they hope to transform. In this paper, I reflect critically on my experiences of a course aimed at transforming teaching through transforming teachers. In this paper, I foreground my own positionality as a white female educator as I draw on feminist theorising to reflect on my experiences as a learner in the Community, Self and Identity course. I suggest that we need to teach in ways that are more cognisant of the complexities of the constraints on personal freedom in the past if we are to contribute to the development of social justice in the future.IS

    Cooking a Pot of Beef Stew: Navigating Through Difficult Times Through Slow Philosophy

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    The Slow Movement offers feminist scholars permission to inhabit multiple identities and recognizes the inherent value of care work as work. Against an intimate living backdrop of pancreatic cancer, COVID-19, and overwork, I practice Slow scholarship by embodied caring for three elders while experiencing powerful anxiety. Identifying as a daughter, mother, carer, student, friend, leader, and scholar, I look to a variety of wisdom sources outside universal concepts of value and time to ground myself in the present. Zen, Taoism, and existentialism suggest staying with anxiety as a viable means to live in an uncomfortable present

    The Economics of Terrorism and Counter-Terrorism: A Survey (Part II)

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    Approach and Selection of Popular Narrative Genre

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    Communism “without Guarantees”

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    Lie for a Dime

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    The Internet has enabled recruitment of large samples with specific characteristics. However, when researchers rely on participant self-report to determine eligibility, data quality depends on participant honesty. Across four studies on Amazon Mechanical Turk, we show that a substantial number of participants misrepresent theoretically relevant characteristics (e.g., demographics, product ownership) to meet eligibility criteria explicit in the studies, inferred by a previous exclusion from the study or inferred in previous experiences with similar studies. When recruiting rare populations, a large proportion of responses can be impostors. We provide recommendations about how to ensure that ineligible participants are excluded that are applicable to a wide variety of data collection efforts, which rely on self-report
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