-  - A diagram that represents broadly the way I think about incentives at three relevant levels for scientific collaboration. #open are these the only levels of scientific collaboration? - Through this diagram, I argue that incentives are rather inherently not scaleable, and different incentives are more in 'action' during different levels of collaboration than they are at other levels. - For example,** at the level of the team**, one way to explore the explanatory challenge of why someone might spend huge amounts of effort doing something that they aren't paid for such as peer review is that among their community - they've cultured an ethic of contributing to the more abstract 'global scientific community' (cite). Or alternatively - the delivery on creating high quality scientific practices which aren't incentivized on a global level is more likely to grow from a community like the one here, one that has created a mission in developing better practices __despite__ the incentives that don't support these practices. - On a slightly higher level -** at the level of the institution **- incentives revolve at the moment around securing tenure, guarenteeing secure funding for your projects and ensuring that you can continue on your work and remain supported. These mechanisms can be direct through social incentives to adhere to various practices in order to be seen as a valuable collaborator (if the institutional community has established practices which you can do to 'navigate' relationships external to your main team) or through monetary/survival incentives which require to to navigate communication, bureaucracy, and regulation to ensure you remain part of the institutional structure.
- I think of Universities like this. There can be university ethics around Open Science and creating a practice of high quality science, and there are also the general rules that are requried for a professor or academic to be apart of the university. Essentially, the university as a institution gets to decide if they grant you tenure, a salary, or a lab in which to work. If you haven't adhered to the rules they have set up (either through direct action, or through no fault of your own) than as the group with power they can use that power to exclude you the academic from the institution + and resources.
- At** the highest level**, these incentives are even further extracted. The most social of these incentives can be seen as 'memes'. Open Science right now seems to be a meme - almost everyone I talk to thinks about how science isn't achieving it's goals, but given the strength of the institutional and team level communities, unless an individual is quite taken by the importance of the meme and is willing to be the person to start that social norm within their team/lab/university they are more likely to adhere to the more direct social rules and regulations which help them navigate their more immediate community. On this level you can set up infrastructures that allow for wider scientific communication and make it easy/standardized/and integrated into scientific practices to practice interdisciplinary best practices, but you likely can't force adoption. In addition, you can create practices of assigning higher clout/reputation/payoffs to those who adhere to the global social memes and create changes within their process, but if a smaller community doesn't require it or actively discourages it (I'm thinking Pharma, few Pharma scientists at this point will be part of the open science movement because their institutional incentives (having a job, lab, etc) are often dependent on their zipped lips).
- The patterns I currently am averse to at this level is the initial adoption of such structures by the most capable. #open Is this bad??. We see this in open source - those with the most time and priviledge are currently the only ones contributing bc those who are alreadly systematically disadvantaged are more likely to get into positions where they have to compromise the moral of sharing information in order to secure a more mainstream or secure job. My hypothesis for why I find this distasteful is because each new economy is established almost entirely by those who were in power in the previous economy (CITE - via Andre's book/convo). This means that the likelihood that these new economies replicate the past economies seems super high. I find this particularly true in the current case of the gig-economy where many people were like - THIS IS GREAT! give everyone the flexibility they need, but this actually created a space where people weren't supported and were spending all of their time trying to find the gigs, or survive on the marginal returns. Thus the powerful - had their security and got some extra, but those who needed the support were now even more a slave to a system which didn't sufficiently compensate them. (CITE??)
- Understanding these levels is essential for understanding how I think about the failings of interdisciplinary science at the moment. Which has more levels - of course :). - When thinking about sharing among different people there are three potential failing points.
- 1. We don't share our knowledge - 2. We don't use the shared knowledge - 3. Our shared knowledge isn't exhaustive of the perspectives that need to be shared
- For the first, Open Science right now seems to go some way towards finding ways to ensure that we share all of our knowledge. Pre-print servers, changing scientific practices arising in a rapidly publishing scientific landscape headed up by genuinely massive journals such as PLOS () or Elife (). But as far as I can tell is that it fails to allow us to actually use or check the exhaustiveness/inclusiveness of this knowledge. - For the second point - there are many more breakdowns. https://www.arthurperret.fr/articles/2022-11-13-researchers-needs-and-options-for-collaborative-synthesis.html - this is a LOVELY article that expresses what's important here but I'll also extract out.
- We need primitives that can be expressed across disciplines. This means FAIR data principles essentially. - We need to be able to Find, Access, Interoperate (connect), and Reproduce data insights. #open - which of these are least explored?? My hypothesis -> find + interoperate are most difficult. I see reproduce + access solved through the open science stuff and ESPECIALLY through the DeSci Labs work. - Data that can be aggregated across all of science
- Now I'll bring in the incentive levels from above. The act of integration happens at the individual level. The act of using integrated knowledge happens at the global level. #open is this actually a problem?. - My current assumptions is that this interdiscipinary synthesis is really difficult because if at the small level you don't have a system to integrate into the global system, than the data won't be interoperable. But also, if you are a small group - you will almost INEVITABLY have a contextual situation in which youre systems are not necessarily compatible with a more 'general' audience. #open - how has this been resolved in the past??