Destaques

Rui Alexandre Grácio [2024]

«1. Automating Research Changes the Definition of Knowledge. (…)
Rather, it is a profound change at the levels of epistemology and ethics. It reframes key questions about the constitution of knowledge, the processes of research, how we should engage with information, and the nature and the categorization of reality. Just as du Gay and Pryke note that ‘accounting tools...do not simply aid the measurement of economic activity, they shape the reality they measure’ (2002, pp. 12-13), so Big Data stakes out new terrains of objects, methods of knowing, and definitions of social life». p. 3

«We must ask difficult questions of Big Data’s models of intelligibility before they crystallize into new orthodoxies. If we return to Ford, his innovation was using the assembly line to break down interconnected, holistic tasks into simple, atomized, mechanistic ones. He did this by designing specialized tools that strongly predetermined and limited the action of the worker. Similarly, the specialized tools of Big Data also have their own inbuilt limitations and restrictions. One is the issue of time. ‘Big Data is about exactly right now, with no historical context that is predictive (…)». p. 4

«2. Claims to Objectivity and Accuracy are Misleading» (p. 4)

«All researchers are interpreters of data. As Lisa Gitelman (2011) observes, data needs to be imagined as data in the first instance, and this process of the imagination of data entails an interpretative base: ‘every discipline and disciplinary institution has its own norms and standards for the imagination of data.’ As computational scientists have started engaging in acts of social science, there is a tendency to claim their work as the business of facts and not interpretation. A model may be mathematically sound, an experiment may seem valid, but as soon as a researcher seeks to understand what it means, the process of interpretation has begun. The design decisions that determine what will be measured also stem from interpretation. (…) Interpretation is at the center of data analysis. Regardless of the size of a data set, it is subject to limitation and bias. Without those biases and limitations being understood and outlined, misinterpretation is the result. Big Data is at its most effective when researchers take account of the complex methodological processes that underlie the analysis of social data». (pp. 5-6)

«3. Bigger Data are Not Always Better Data. (…) Unfortunately, some who are embracing Big Data presume the core methodological issues in the social sciences are no longer relevant. There is a problematic underlying ethos that bigger is better, that quantity necessarily means quality». (p. 6

«4. Not All Data Are Equivalent
Some researchers assume that analyses done with small data can be done better with Big Data. This argument also presumes that data is interchangeable. Yet, taken out of context, data lose meaning and value. Context matters» (p. 8)

«Fascinating network analysis can be done with behavioral and articulated networks. But there is a risk in an era of Big Data of treating every connection as equivalent to every other connection, of assuming frequency of contact is equivalent to strength of relationship, and of believing that an absence of connection indicates a relationship should be made. Data is not generic. There is value to analyzing data abstractions, yet the context remains critical». (p. 10)

«5. Just Because it is Accessible Doesn’t Make it Ethical. (…) 
It may be unreasonable to ask researchers to obtain consent from every person who posts a tweet, but it is unethical for researchers to justify their actions as ethical simply because the data is accessible.» (pp. 10-11)

«6. Limited Access to Big Data Creates New Digital Divides. (…)
The era of Big Data has only just begun, but it is already important that we start questioning the assumptions, values, and biases of this new wave of research». (pp. 12-13)