The Impact of Convictions on Interlocking Systems

Abstract

What gives a researcher the conviction that a project deserves the time spent collecting data—or does the data itself inspire the research? Conviction, in this context, refers to the confidence that the data will potentially inform or enhance the work in a given field (a system). While objectivity about the collection process itself requires integrity, the decision to apply for funding and move forward requires this more elusive sense of commitment. Discussions about integrity in research assume a universal standard, but only recently have studies examined the varied interpretations of "integrity." More than a moral code, more than a lack of statistical bias, to most researchers, integrity may imply response to an undefinable sense of "truth" (Shaw, Satalkar 2018). Today's constantly changing conditions remain fraught with decisions about topical relevance, questions of bias, and the caution not to act on outdated statistics that confirm our worst assumptions and confuse questions of "truth" (Rosling 2018). This paper draws on research in systems theory, health informatics, environmental and behavioral science, and transdisciplinary education to define an analog for long-term research in which the data itself inspired the conviction to sustain a project with counterintuitive data. Once set in motion, the pattern of sustainability redefined expectations, thus launching parallel research—imitable patterns of hopeful action--in surrounding systems, each driven by new observations and statistics. In these transdisciplinary examples, decisions to expand problem-solving contexts or hypotheses resulted from an analog built loosely on these steps: Statistics-gathering; Collaboration and interpretation of data; Conviction of a need to replicate the results, based on the data; Adaptation of the project (and the thinking) based on the data; Stakeholder actions based on confidence in the data; Long-term impacting one field; and finally, Mimicry or movement in parallel fields of research or institutions or locations, based on the results of the prior steps. In the best-case scenarios cited, a project grounded in data affirms hope and leads to resilience or sustainability over time and across disciplines and interlocking systems (Goodall, 2021, Rosling, 2018, Ribeiro 2021, Langness 2020, Platt 2022).