Article by Patrick Dunleavy: “Open social science is new, and like any beginner is still finding its way. However, to a large extent we are still operating in the shadow of open science (OS) in the Science, technology, engineering, mathematics, and medicine, or STEMM, disciplines. Nearly a decade ago an influential Royal Society report argued:
‘Open science is often effective in stimulating scientific discovery, [and] it may also help to deter, detect and stamp out bad science. Openness facilitates a systemic integrity that is conducive to early identification of error, malpractice and fraud, and therefore deters them. But this kind of transparency only works when openness meets standards of intelligibility and assessability – where there is intelligent openness’.
More recently, the Turing Way project defined open science far more broadly as a range of measures encouraging reproducibility, replication, robustness, and the generalisability of research. Alongside CIVICA researchers we have put forward an agenda for progressing open social science in line with these ambitions. Yet for open social science to take root it must develop an ‘intelligent’ concept of openness, one that is adapted to the wide range of concerns that our discipline group addresses, and is appropriate for the sharply varying conditions in which social research must be carried out.
This task has been made more difficult by a number of premature and partial efforts to ‘graft’ an ‘open science’ concept from STEMM disciplines onto the social sciences. Three false starts have already been made and have created misconceptions about open social science. Below, I want to show how each of the strategies may actually work to obstruct the wider development of open social science.
Bricolage – Reading across directly from STEMM
This approach sees open social science as just about picking up (not quite at random) the best-known or most discussed individual components of open science in STEMM disciplines – focusing on specific things like open access publishing, the FAIR principles for data management, replication studies, or the pre-registration of hypotheses…(More)”.