Confronting Research Misconduct in Citizen Science

So, you suspect that someone in a citizen science project committed research misconduct. What do you do now? As citizen science methods become increasingly popular, it seems inevitable that at some point, someone identifying themselves as a citizen scientist will be accused of committing research misconduct. Yet the growth of the field also takes research increasingly outside of traditional regulatory mechanisms of identifying, investigating, and delivering consequences for research misconduct. How could we prevent or handle an allegation of scientific misconduct in citizen science that falls outside of our familiar regulatory remedies? And more broadly, what does this imply for ensuring scientific integrity in citizen science? I argue that the increasing use of new research methods in citizen science poses a challenge to traditional approaches to research misconduct, and that we should consider how to confront issues of research misconduct in citizen science. I briefly describe existing approaches to research misconduct and some aspects of citizen science giving rise to the problem, then consider alternative mechanisms, ranging from tort law to professional responsibility to a proposed “research integrity insurance,” that might be deployed to address and prevent such cases

The Relationship Between Financial Performance, Firm Size, Leverage and Corporate Social Responsibility

Approximately $25.2 trillion in total assets under management in the United States is involved in some strategy of socially responsible and sustainable investing. Grounded in the stakeholder theory, the purpose of this correlational study was to examine the relationships between financial performance, firm size, leverage, and corporate social responsibility. A random sample included 119 large companies located in the United States from the population of companies listed in the Russell 100 index. The data were collected via Bloomberg Terminal. Multiple linear regression analysis was used to predict Environmental, Social, and Governance (ESG) activity scores. The 3 predictor variables accounted for approximately 7% of the variance in ESG activity scores and the result was statistically significant, F(3,115) = 2.83, p \u3c .04, R2 = .07. Although the p value was significant, the R2 was low representing a poor model fit. In the final analysis, total revenue was added to the model and was a significant predictor and negatively correlated with ESG activity scores; However, return on equity and leverage were not significant predictors of ESG activity scores suggesting the potential need to transfer some corporate social initiatives from business leaders to government policy makers. Future researchers should consider incorporating additional variables to make the model more useful. The implications for positive social change include the potential to identify fiscal incentives for corporate social programs by policy makers which benefit stakeholders such as employees, suppliers, customers, communities, and the environment