| Literature DB >> 25354280 |
Cliodhna O'Connor1, Helene Joffe1.
Abstract
Neuroscience research on sex difference is currently a controversial field, frequently accused of purveying a 'neurosexism' that functions to naturalise gender inequalities. However, there has been little empirical investigation of how information about neurobiological sex difference is interpreted within wider society. This paper presents a case study that tracks the journey of one high-profile study of neurobiological sex differences from its scientific publication through various layers of the public domain. A content analysis was performed to ascertain how the study was represented in five domains of communication: the original scientific article, a press release, the traditional news media, online reader comments and blog entries. Analysis suggested that scientific research on sex difference offers an opportunity to rehearse abiding cultural understandings of gender. In both scientific and popular contexts, traditional gender stereotypes were projected onto the novel scientific information, which was harnessed to demonstrate the factual truth and normative legitimacy of these beliefs. Though strains of misogyny were evident within the readers' comments, most discussion of the study took pains to portray the sexes' unique abilities as equal and 'complementary'. However, this content often resembled a form of benevolent sexism, in which praise of women's social-emotional skills compensated for their relegation from more esteemed trait-domains, such as rationality and productivity. The paper suggests that embedding these stereotype patterns in neuroscience may intensify their rhetorical potency by lending them the epistemic authority of science. It argues that the neuroscience of sex difference does not merely reflect, but can actively shape the gender norms of contemporary society.Entities:
Mesh:
Year: 2014 PMID: 25354280 PMCID: PMC4212998 DOI: 10.1371/journal.pone.0110830
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Figure 1Sample size (number of data units) of each dataset.
Figure 2Prevalence of reference to the various behavioural domains across the datasets.
Figure 3Prevalence of causal attributions for sex difference across the datasets.
Figure 4Prevalence of the various modes of framing difference across the datasets.
Figure 5Prevalence of instances of differential valuation of the sexes across the datasets.
Figure 6Prevalence of reference to various dimensions of gender politics across the datasets.