From an Individualistic to a Collectivistic Mindset in Open Research

In their article on open research, Marsden and Morgan-Short discuss the challenges and opportunities associated with open research practices. I have always been committed to the principles of the open research movement but putting them into practice can be difficult. Publishing my research instruments and materials was uncomfortable at first. Like so many of my colleagues, I was concerned that other researchers would scrutinize my materials and find a critical flaw. When I started publishing data and analyses in the form of R code, I had similar fears. At the time, I had not heard of researcher degrees of freedom, so I had not reflected on the fact that research is not quite as objective as it may at first seem because, at the end of the day, researchers make semi-subjective determinations about who to include in their studies, what to measure and how to do so, and how to analyze the data (Coretta et al., 2022). Indeed, this element of choice is what makes research exciting, and it is also what makes open practices indispensable. I am happy to report that my fears surrounding open research have been allayed. Instead of intense criticism, I have received thoughtful feedback and encouragement. In some cases, I have even received R code that was helpful for furthering my analyses. Above all, the support that I have received has convinced me that researchers’ strength lies not just in their collective expertise, but also in their commitment to one another. Clearly, there is potential for individual researchers to engage in open practices, making their materials, data, and analyses publicly accessible, and individual pathways to contribution are well known. But an exciting new direction in the open research movement—one that is not so new, perhaps, but increasingly valued—is toward a collective sensibility wherein teams of researchers work together to collect, aggregate, and analyze data, yielding findings that are more inclusive and representative of the diverse populations with whom they are beginning to work and that are more statistically reliable and precise. Importantly, through a community-driven approach to open science within which researchers form larger, more representative research teams to collect larger, more representative datasets, they can begin to ask entirely new research questions and fit more interesting and sophisticated statistical models, models that reflect the types of relationships researchers believe to exist but until now have been unable to test. In applied linguistics research to date, barring a few notable exceptions, it has been common to collect and analyze relatively small datasets of 20–30 participants per group and to do so individually. In making this observation, I do not mean to criticize this approach in any way because for many years it was arguably the only approach that was practically and professionally viable. But this is no longer the case. Collaborative work is increasingly valued and accepted. At the same time, there is still an emphasis on the quantification of individual research products. For collaborative work, quantification often amounts to trying to assign percent effort (e.g., Author A was responsible for 42% of the work, Author B 38%, and Author C 20%). This type of quantification sees the individual as the locus of effort rather than the team, failing to acknowledge that the whole is greater than the sum of its parts. Returning to the idea of percent effort, it seems entirely reasonable for several authors to invest 100% because what they produce together far eclipses what they would have produced independently, and I have argued as much on at least one occasion. How, then, do researchers get from where they are now, where collaboration is valued as long as scholars produce individual publications to “prove” their intellectual worth, to a conceptual and professional space in which large-scale, team-based, open projects are seen as vehicles for creating a more inclusive, professionally rewarding, and innovative and impactful research world? In other words, why is a shift from an individualistic to a collectivistic approach worthwhile? First, by working collectively, researchers can create inclusive training and mentorship structures that allow them to gain new insight into the research process and engage with experts who can guide and support them. Second, researchers can crowdsource the best materials and make them openly accessible, which feeds back into the profession in two ways: Novice researchers can use these instruments, reducing barriers to entry, and by using a common set of materials researchers can generate findings that are maximally comparable. This endeavor dovetails neatly with calls for increased replication research. Researchers are currently producing large quantities of new materials, in part because innovation continues to be prioritized above the gradual accumulation of empirical evidence. Simply put, in the intellectual economy in which researchers work, novel ideas are their professional capital, so they create new instruments and modify existing ones, making changes that may be minor but are certainly not trivial, to pursue new research agendas. Certainly, there will always be a place for such work, and researchers should in no way stifle innovation. But rather than generating more and more instruments to be stored, tagged, and searched, it may be time to slow down the pace of innovation and accelerate the pace of instrument validation, investing researchers’ collective energy in curating a set of validated and open research tools to be used in future work. Work in this area is already underway (Mora-Plaza et al., 2022), but it is worth considering how researchers can further systematize and incentivize it on a larger scale. Last, by working together researchers can build larger, more representative datasets that will allow them to ask and answer the types of research questions that are likely to drive the field forward. And if they make these datasets publicly available, then researchers with other interests can leverage the data to address other research questions. In fact, researchers can begin this process right now without necessarily collecting new data. I am certain that within their disciplines, researchers have datasets that could be meaningfully aggregated, which could allow for an easier entry point into this new and exciting research world centered on large-scale research teams and communities rather than on individuals. In closing, all change takes effort, so researchers in applied linguistics should commit to reinvesting in one another to build a more equitable, open, and accessible research model not as individuals but collectively, as a community of engaged scholars and practitioners

Collectivism · Epistemology · Individualism · Linguistics · Mindset · Pedagogy · Natural Language Processing Techniques · Psychology · Second Language Acquisition and Learning · Second Language Learning and Teaching · Social Psychology