In Hegel you have essentially two actors encountering one another and each is a subject, but in order to be a full subject, each needs to be recognized by the other. Each affirms the other as a subject in its own right that is simultaneously equal and different from me. If both people can affirm that, then you have a reciprocal egalitarian, symmetrical process of recognition. But, famously, in the master-slave dialectic, they encounter one another on highly asymmetrical, unequal terms, terms of domination or subordination. Then you get non-reciprocal recognition.
Historicism especially as expressed in the work of Wilhelm Dilthey, immediately preceded the sociology of knowledge. The dominant theme here was an overwhelming sense of the relativity of all perspectives on human events, that is, of the inevitable historicity of human thought. The historicist insistence that no historical situation could be understood except in its own terms could readily be translated into an emphasis on the social situation of thought. Certain historicist concepts such as “situational determination” and “seat in life” could be directly translated as referring to the “social location” of thought.
In a previous post it was addressed the concept of experimental governance, understood “as a means to launch an environmental project in spite of uncertainties and uphold the project without disrupting the overall process” (Gross, M., & Heinrichs, 2010:283). This point, the authors continues “is wholly pragmatic to create and facilitate the building of a community of inquirers who locally deliberate social problems, form hypothesis about appropiate means and ends of practice, and put their assumptions to test”.
In this context, insofar non-scientist community members are enriching the research process with “pre-scientific” knowledge (formation of hypothesis and ends of practics to be test) they are taking actively part of such process. This moves away the experimental governance from the Habermas communicative approach or “participatory paradigm”. The pragmatist ideas developed by Habermas “have trickled down to environmental planning discourse since the 1970s and researchin environmental sociology has examined a wide range of participatory decision processes” (Gross, M., & Heinrichs, 2010:282). However, the authors argue, in the ideal case, it is not enough to bring local actors into deliberation where their varying presumptions and biases will succumb to the force of the better argument (by scientist and practicioners?). Hence, the actual power to have a say in political decision making is easily taken away from the participants (the lack of arguments among local actors and the consistent of the scientifist discourse ultimate take the former ones away from decision making. Public participation is reduced to a information session where scientist show how powerful they are in base of their consistent discourse). Furthermore, the authors suggest that the Habermassian ideal type case could not be further from real-world decision making which is characterized by many unknows and uncertainies that cannot even be fathomed via risk assessment and computer modeling, let alone by mere citizen participation.
But the experimental governance consists of not only bring local actors into deliberation but also allow them to “form hypothesis about appropiate means and ends of practice, and put their assumptions to test”. In other words, the experimental governance consist of allowing local actors for forming hypothesis based on their everyday experience, i.e. pre-scientific knowledge, as a previous step to objetivize the phenomon, it is, to produce scientific knowledge.
Gross, M., & Heinrichs, H. (Eds.). (2010). Environmental sociology: European perspectives and interdisciplinary challenges. Springer Science & Business Media.
In this provocative and broad-ranging work, the authors argue that the ways in which knowledge – scientific, social and cultural – is produced are undergoing fundamental changes at the end of the twentieth century. They claim that these changes mark a distinct shift into a new mode of knowledge production which is replacing or reforming established institutions, disciplines, practices and policies.
Identifying features of the new mode of knowledge production – reflexivity, transdisciplinarity, heterogeneity – the authors show how these features connect with the changing role of knowledge in social relations. While the knowledge produced by research and development in science and technology is accorded central concern, the authors also outline the changing dimensions of social scientific and humanities knowledge and the relations between the production of knowledge and its dissemination through education.
“Re-Thinking Science” presents an account of the dynamic relationship between society and science. Despite the mounting evidence of a much closer, interactive relationship between society and science, current debate still seems to turn on the need to maintain a ‘line’ to demarcate them. The view persists that there is a one-way communication flow from science to society – with scant attention given to the ways in which society communicates with science.
The authors argue that changes in society now make such communications both more likely and more numerous, and that this is transforming science not only in its research practices and the institutions that support it but also deep in its epistemological core. To explain these changes, Nowotny, Scott and Gibbons have developed an open, dynamic framework for re-thinking science.
The authors conclude that the line which formerly demarcated society from science is regularly transgressed and that the resulting closer interaction of science and society signals the emergence of a new kind of science: contextualized or context-sensitive science. The co-evolution between society and science requires a more or less complete re-thinking of the basis on which a new social contract between science and society might be constructed. In their discussion the authors present some of the elements that would comprise this new social contract.
I have recently referred to an interview made to Piketty where he states “there is no such thing as economic science. There are social sciences”. He argues that “economic processes involve social control” and that “we should teach economics much more in conjunction with economic history, social history, political history, political science”
That said, the truth is that Piketty’s argument is deductible from the classic economic sociology concept embeddedness. It refers to the degree to which economic activity is constrained by non-economic institutions. The term was created by economic historian Karl Polanyi as part of his Substantivist approach. Polanyi argued that in non-market societies there are no pure economic institutions to which formal economic models can be applied. In these cases economic activities such as “provisioning” are “embedded” in non-economic kinship, religious and political institutions. In market societies, in contrast, economic activities have been rationalized, and economic action is “disembedded” from society and able to follow its own distinctive logic, captured in economic modeling. Polanyi’s ideas were widely adopted and discussed in anthropology in what has been called the “Formalist vs Substantivist” debate. Subsequently, the term “embeddedness” was further developed by economic sociologist Mark Granovetter, who argued that even in market societies, economic activity is not as disembedded from society as economic models would suggest.
“Spurious correlations” is the name of a website I came across recently. There you can see plenty of cases where correlation may not imply causation. What does it mean in terms of research methods in the social sciences? It means that whenever our research approach is uniquely quantitative, we take the risk of come up with wrong or non-consistent findings. In other words, a certain doses of qualitative interpretation is always required in order to avoid wrong and sometimes ridiculous depictions of the social reality, like in the bellow example. It is true that advance and multivariable analysis allow having a more accurate understanding of certain social phenomena nowadays. However such analysis should always be preceded by a good interpretation of such phenomenon. What does “good” mean? Well, first of all take into consideration whether two or more variable are susceptible to be part of the same social reality. It doesn’t seem that “US spending on science, space, and technology” and “Suicides by hanging, strangulation and suffocation” has anything to do one each other. Secondly, even when two variables may potentially explain certain realities we should try to include more than one explicative variable. For instance, when studying the reality of Polish labor market, we may find correlation between education and employment. However, an accurate analysis should also include such other variables as age, income or social class. Such analysis are possible via the so called multivariate statistical analysis to be hopefully covered in future posts. The key of this sort of analysis is considering the right variables. To do so, we can use previous theories, where certain authors (based on previous empirical studies) suggest a number of a priori relevant and explicative variables.
“Recently, it was conducted a global survey which sought to answer the following question: Please answer honestly. How in your opinion could be solved the problem of lack of food in many countries in the world?”
The survey was a failure because in Africa nobody knew what food means. In France nobody knew what honesty means. Nobody knew in Western Europe what having lack of something means. In China no one knew what is having your own opinion. In Arabs countries none knew what is solving a problem. In South America, nobody knew what the word “please” means. In North America, no one knew that other countries exist.”
The above parody is just that, a parody. However, it illustrates very well how the ambiguity of such terms as happiness, leadership or being modern are constantly challenging social researchers. “The more ambiguous and elastic our concepts, the less possible is to quantify our data in a meaningful way” (Dey, 1993) Can we measure happiness all over the world if the meaning of it may strongly vary from one country to another? It may be not possible assuming right away a quantitative approach. And it is precisely here where the qualitative one finds its place. Qualitative techniques may bring the not measurable concepts into the “realm” of the measurable. Indeed it is “an opportunity to explore a subject in as real manner as is possible” (Robson, 2002).
For instance, what is a great place to work? We can quantify work by the unemployment rate in every country, as well as places where people work just collecting information on the number of companies. But great place to work? We could actually build another funny story as above, or just try to understand what people think what a “great place to work” is. And this is precisely what Great Place to Work did to measure a prior ambiguous concept.
This company on a year basis publishes a best workplace ranking both nationwide and worldwide. To do so they built up a model formed by categories of analysis like trust, enjoy or pride, among others. In turn each of them is divided into indicators. These indicators give way to a questionnaire that is used in a survey among a set of companies in every country. As they report in their website, “this model has been confirmed through over 25 years´ worth of analysis of employees´ own opinions“.
Therefore, the quantification of a priori ambiguous concepts is preceded (or even accompanied) by a qualitative analysis. This analysis consists of doing a categorization of what people think it is a great place to work. If the result of a quantitative study is usually a graphic or a statistic table, the result of a qualitative study is based on categorizations.
Finally, although the categorization process is frequently used as way to identify measurable units referred to ambiguous concepts, as in the above example, it may be used independently, i.e. doing a categorization of happiness, for instance, may have as the only aim to obtain an accurate understanding of this concept, regardless it is going to be or not measured later.
- The meaning of qualitative methods (researchmethodsgdansk.wordpress.com)
- Why qualitative research? (Case study and solution) (researchmethodsgdansk.wordpress.com)
- Qualitative vs. quantitative debate or why facebook is not driving the debt crisis (researchmethodsgdansk.wordpress.com)
(Needless to say that if case Qualitative would be feminine and quantitative masculine)
-Mrs. Qualitative: why?
-Mr. Quantitative: obviously, to be able to do law-like generalizations like natural scientist do!
-Mrs. Qualitative: It is not possible because relationships of human beings might be spurious.
– Mr. Quantitative: I don´t understand.
– Mrs. Qualitative: we don´t need to be like natural sciences and do law-like generalizations, we just need to get a more accurate understanding of the phenomenon under study, as well as reflect on and interpret it.