One of the drivers for pushing for open data as a form of corruption control stems from the belief that in making government operations more transparent, it would be possible to hold public officials accountable for how public resources are spent. These large datasets would then be open to the public for scrutiny and analysis, resulting in lower levels of corruption. Though data quality has been largely studied and many advancements have been made, it has not been extensively applied to open data, with some aspects of data quality receiving more attention than others. One key aspect however—accuracy—seems to have been overlooked. This gap resulted in our inquiry: how is accurate open data produced and how might breakdowns in this process introduce opportunities for corruption? We study a government agency situated within the Brazilian Federal Government in order to understand in what ways is accuracy compromised. Adopting a distributed cognition (DCog) theoretical framework, we found that the production of open data is not a neutral activity, instead it is a distributed process performed by individuals and artifacts. This distributed cognitive process creates opportunities for data to be concealed and misrepresented. Two models mapping data production were generated, the combination of which provided an insight into how cognitive processes are distributed, how data flow, are transformed, stored, and processed, and what instances provide opportunities for data inaccuracies and misrepresentations to occur. The results obtained have the potential to aid policymakers in improving data accuracy.

A prominent question in social epistemology concerns the epistemic profile of groups. While inflationists and deflationists agree that groups are fit to constitute knowers, they disagree about whether group knowledge is reducible to knowledge of their individual members. This paper develops and defends a weak inflationist view according to which some, but not all, group knowledge is over and above any knowledge of their members. This view sits between the deflationist view that all group knowledge is reducible to individual knowledge, and the strong inflationist view that some such knowledge even fails to supervene on features of individuals. Thus, some group knowledge is irreducible, but all such knowledge is anchored in, and so doesn't float freely from, individual features.

Engineering design teams, particularly in academic courses, were no exception to the consequences of the unknown virus and the global pandemic. Forced online teaching has influenced the way of using communication technologies. The information flow architecture of engineering design is also transforming due to the remote activities and the dominancy of web-based technologies. This transformation creates different patterns of distributed cognition within design teams. In the course of full remote teaching, we studied the entire information flow of a small and dispersed engineering team through the early stages of design for one month using the ethnographic method and Distributed Cognition analysis techniques. Our analysis, of the interdisciplinary design team during a rocket engineering project and system engineering teaching, shows the considerable role of different online data sharing and communications technology platforms in distributed cognition and collaborative problem solving within the team. These new trends create new challenges and opportunities, and in order to enhance collaborative design, these emerged out of the box trends require more attention and updating of existing strategies.

While acknowledging that they have many points of theoretical agreement, the discussion deliberately focuses on areas of incompatibility between integrational and ‘distributed’ approaches to mind, cognition, and language in the light of Roy Harris’ claim that the notion of a ‘distributed mind’ comprises a category mistake. Harris’ position is based on his affirmation of a ‘vulgar concept of mind’ which contrasts sharply with certain accounts of mind originating from within cognitive science. The tension between lay and scientific understandings of mind and language forms a key point of discord between the two approaches. I discuss Harris’ argument that the category mistake inherent in the notion of ‘distributed mind’ can be dissolved by replacing it with the notion of an ‘integrated’ or ‘integrating’ mind. I then consider the derived notion of ‘distributed language’ and its theoretical and explanatory value from an integrational perspective. Finally, I conclude by arguing that although they share many important insights, the differences of perspective which exist between the integrational and distributed approaches are such as likely to prevent the emergence of anything resembling a unified movement.