The Internet Model = why Open Access is not enough
Publishing and peer review processes in academia are currently closed models. In my view, at least in the areas i operate in (social sciences and humanities), these processes should be far more, if not entirely, open, with a provision for privacy in special cases. I call this model Open-process academic publishing.The name deliberately distinguishes it fromOpen Access, which refers to only the final outcome of academic knowledge production being open. The suggestion is not to open the processes in random ways, but in ways in which this openness — fundamentally based on volunteer participation — brings/enables more structure, more internalized working discipline, more commitment, and more ability to improve cooperation/collaboration with deliberate precision – all with the goal of improving the outcomes. “[…] culture of open processes was essential in enabling the Internet to grow and evolve as spectacularly as it has”, hence, we could call it The Internet Model (software/FS + networking/IETF). Its potential screams for being reused, hacked, for other areas of production. Academia, especially its publishing side, seems to me capable of embracing such volunteer-core open-process cooperation …
… four fundamental goals:
- Transparency in experimental methodology, observation, and collection of data.
- Public availability and reusability of scientific data.
- Public accessibility and transparency of scientific communication.
- Using web-based tools to facilitate scientific collaboration.
The idea I’ve been most involved with is the first one, since granting access to source code is really equivalent to publishing your methodology when the kind of science you do involves numerical experiments. I’m an extremist on this point, because without access to the source for the programs we use, we rely on faith in the coding abilities of other people to carry out our numerical experiments. In some extreme cases (i.e. when simulation codes or parameter files are proprietary or are hidden by their owners), numerical experimentation isn’t even science. A “secret” experimental design doesn’t give skeptics the ability to repeat (and hopefully verify) your experiment, and the same is true with numerical experiments. Science has to be “verifiable in practice” as well as “verifiable in principle”.
In general, we’re moving towards an era of greater transparency in all of these topics (methodology, data, communication, and collaboration). The problems we face in gaining widespread support for Open Science are really about incentives and sustainability. How can we design or modify the scientific reward systems to make these four activities the natural state of affairs for scientists? Right now, there are some clear disincentives to participating in these activities. Scientists are people, and we’re motivated by most of the same things as normal people:
- Money, for ourselves, for our groups, and to support our science.
- Reputation, which is usually (but not necessarily) measured by citations, h-indices, download counts, placement of students, etc.
- Sufficient time, space, and resources to think and do our research (which is, in many ways, the most powerful motivator).
Right now, the incentive network that scientists work under seems to favor “closed” science. Scientific productivity is measured by the number of papers in traditional journals with high impact factors, and the importance of a scientists work is measured by citation count. Both of these measures help determine funding and promotions at most institutions, and doing open science is either neutral or damaging by these measures. Time spent cleaning up code for release, or setting up a microscopy image database, or writing a blog is time spent away from writing a proposal or paper. The “open” parts of doing science just aren’t part of the incentive structure.