Networking: PP4 and research success

I was very intrigued by Sarah Reardon‘s piece in Nature, on now networks influence women’s success in science.  It was tweeted by Athene Donald, who knows a thing or two herself about networking and science, and whose valuable tweets I’ve followed for sometime .

The piece is a powerful illustration of Paula Principle factor 4, the influence of vertical networks.  It took me back, almost nostalgically, to the turn of the century when with John Field and others I was directly engaged in promoting the notion of social capital as a useful tool of analysis.  The unsurprising finding is how often women working in scientific teams have found their contributions ignored or unrecognised.  The more contentious issue arising is how far women can and should build their own networks, rather than relying on ‘linking’ social capital which to some extent inevitably ties their fortunes to those of men higher up in the scientific hierarchy (an effect which will of course decline, but only slowly).  

Reardon’s piece refers to a controversy over the publication, and subsequent withdrawal, of a large-scale meta-analysis (and I do mean large-scale – 215 million authors on 220 million papers on several subjects over 100 years), on the effects of joint authorship.  This apparently concluded that women did better if they had male mentors and co-authors;  but was subsequently withdrawn after criticisms both of its methodology and its political implications.  

It’s easy to see how disturbing such findings might be.  I read this piece directly after reading a chapter critiquing scientific institutions and practices, in Iain McGilchrist’s magisterial The Matter with Things. He gives some hair-raising illustrations of how methodologies such as data mining, where large datasets are scoured to find statistical correlations which may, but may not, have any substantive significance. My guess is that the findings would only be useful if the century-long data is carefully used to show what changes there have been over time.

The examples of Nobel prizewinning collaborators Jennifer Doudna and Emmanuelle Charpentier are genuinely inspirational. it’s certain both that women will increasingly build their own networks, and that networks in general will become increasingly mixed in terms of women and men occupying varied positions in the hierarchy.   But this is not to underplay the influence that gender-imbalanced networks will continue to have on the recognition and valuing of women’s competences. Reardon’s piece focussed on science networks; other disciplines are maybe less dependent on multiple authorships and team research, but it would still be interested to explore the same issues in relation to, say, the success of social scientists.