A new scientific paper finds that incorporating sex and gender could improve experiments, reduce bias and create opportunities for discovery and innovation.
The paper, published in Nature, the world’s leading multidisciplinary science journal, finds that researchers’ sex can affect how they interpret their observations.
According to co-author Dr Robert Ellis, of the University of Exeter. “It’s striking to what degree sex and gender are overlooked in science.”
Dr Ellis gives a telling narrative about the consequences of having these systemic blindspots.
“The original crash test dummies were based on a male physique,” he said. Consequently, female drivers were 47% more likely than males to suffer severe injuries in a comparable crash. This is the result of the scientific community’s habit of universalising the male.
“Sex or gender analysis can be critical to the interpretation, validation, reproducibility and generalizability of research findings,” the study claimed.
“Integrating sex and gender analysis into research design has the potential to offer new perspectives, pose new questions and, importantly, enhance social equalities by ensuring that research findings are applicable across the whole of society.”
It’s a problem that’s been occuring for centuries given science has historically been practiced by straight white males, who are surrounded by people like them, leading to assumptions that the rest of the world is like them. They see themselves and those around them as emblematic of the ‘neutral subject’.
The paper provides yet more proof on why women’s lack of participation in tech and science has real consequences.
We know this, and yet stigma continues to brew among female professionals across STEM industries. Sexual harassment and discrimination are higher among female STEM professions, and systemic barriers continue to prevent or discourage women from full participation.
The need to account for differences in sex in research methods seems like common sense.
This research paper will hopefully encourage a more perceptive, sensitive interpretation of the information that’s being analysed. Enabling social equality requires a systemic shift in the way data and technique are programmed, acknowledging the gender and sex of these individuals and implementing robust methods of analysis centred on sex and gender.