From: "Londa Schiebinger" <schieb@stanford.edu<
To: "genderedinnovations@lists.stanford.edu" <genderedinnovations@lists.stanford.edu<;
Sent: 2023-02-19 (일) 04:02:20 (UTC+09:00)
Subject: [Gendered Innovations] Some research of note
**The Gender and Medicine Network of Swiss Universities has developed a Core Curriculum to support integrating sex and gender issues into the teaching of medical and nursing students in Swiss higher education: https://www.gems-platform.ch/sites/default/files/documents/reference-documents/S%26G_Core_Curriculum_V_nov2022.pdf. More information is here: https://www.gems-platform.ch/en/reference-documents
Addressing sex bias in biological databases worldwide
V Ruiz-Serra, N Buslón, OR Philippe, D Saby… - 2023
… According to Schiebinger, it is necessary to fix the knowledge to stimulate more
responsible science and technology (GRAF Software Documentation, nd), being
biomedical metadata reporting an example not to be overlooked. This needs to be …
Instructions, commands, and coercive control: a critical discourse analysis of the textbook representation of the living cell
C Navare - Cultural Studies of Science Education, 2023
A number of scholars have shown the prevalence of multiple metaphors in biological
conceptions of the living cell. However, there is little research into unpacking these
metaphors to understand their ideological implications for scientific knowledge and
Gender and the Ocean: Marine Resources and Spaces for All
S Harper, A Martin - The Ocean and Us, 2023
The typical media image of a seafarer or crew on a deep-sea trawler is often of a
tough, masculine figure. Many ocean related activities and industries have
historically been, and in some cases continue to be, male dominated. However, as …
Equal accuracy for Andrew and Abubakar—detecting and mitigating bias in name-ethnicity classification algorithms
L Hafner, TP Peifer, FS Hafner - AI & SOCIETY, 2023
Uncovering the world’s ethnic inequalities is hampered by a lack of ethnicity-annotated datasets. Name-ethnicity classifiers (NECs) can help, as they are able to infer people’s ethnicities from their names. However, since the latest generation of NECs rely on machine learning and artificial intelligence (AI), they may suffer from the same racist and sexist biases found in many AIs. Therefore, this paper offers an algorithmic fairness audit of three NECs. It finds that the UK-Census-trained EthnicityEstimator displays large accuracy biases with regards to ethnicity, but relatively less among gender and age groups. In contrast, the Twitter-trained NamePrism and the Wikipedia-trained Ethnicolr are more balanced among ethnicity, but less among gender and age. We relate these biases to global power structures manifested in naming conventions and NECs’ input distribution of names.
GRANTeD-Grant Allocation Disparities from a Gender Perspective: Synthesis report on contextual factors, gender equality policy analysis and gender bias risk …
L Husu, H Peterson - 2022
The project GRANteD (Grant Allocation Disparities from a Gender Perspective)
started in January 2019, funded within the European Commission Horizon 2020
programme, to analyse the occurrence and causes of gender bias in research …
The Robot-Gender Divide: How and Why Men and Women Differ in Their Attitudes Toward Social Robots
E Kislev - Social Science Computer Review, 2023
Recent developments foretell that social robots will soon become an integral part of
everyday life, offering companionship and intimate closeness of different kinds.
While research thus far is limited in scope and data, the current research offers two …
Is Chronic Pain as an Autoimmune Disease?
G Singh - Canadian Journal of Pain, 2023
Autoimmune diseases frequently occurs in females, and a parallel sexually
dimorphic suffering is observed in individuals who suffer chronic pain. While
perception and environment influence the chronicity of pain, this review illustrates
All best, Londa
Londa Schiebinger
Director, EU/US Gendered Innovations in Science, Health & Medicine, Engineering, and Environment Project
John L. Hinds Professor of History of Science, Stanford University
http://www.stanford.edu/dept/HPST/schiebinger.html
