What is Scientific? And what is not?
A scientific study was often contrasted with the study of the social sciences. It was said that the norms of the social sciences were not as rigid as the laws of the sciences. The sciences (i.e. the natural science) could have laws which would hold true for all times, which would not be affected by the researchers biases and could be verified by objective tests. Over the last few decades this notion is loosing validity rapidly. Post-relativity studies, it has been asserted that even the most scientific of disciplines, physics could involve unstable laws. In fact Einstein's relativity has in effect proved the premises of Newton's theory incorrect. The laws of science then, are not universal, at least not in terms of time. A deeper look at the Newton-Einstein controversy would reveal that the two great physicists were in fact not even attempting to explain the same reality. The universality of space also is thus defunct. What then can be called 'scientific'? Or should we slide to the extreme relativist position claiming that there is no truth or that there is truth in everything? Should the attempts at finding an 'objective' analysis be given up?
The Dilemma of the Method in Academic Research
Now that the hegemony of the scientific method no longer exists, how does academic research proceed? There is a huge amount of academic energy invested in the question of methodology. In the absence of a written down thumb rule scientific method, what will come to become an acceptable method, or at least an acceptable premise to validate academic research?
What then will make a Method Scientific?
That there are methods, employed by certain disciplines which yield more acceptable, more objective and more stable results than some others is undeniable. The salience of these methods may not be in the features that we listed above (objectivity, universality and verifiability) but in features other than these.
The Fluidity of the Scientific Method
The humanities choose to call themselves the 'social sciences' since the method of their investigation is deemed 'scientific'. The exchange between the sciences and humanities is however not only one-way. The sciences, especially after the Chaos theory, Relativity and Quantum studies, have close links, methodically with the social sciences. It is no coincidence that relativity and the stream of consciousness novels were expounded at the same time. What we then call a 'scientific method' has elements of both the social sciences and the natural or pure sciences.
What about Technology?
While the method of science has been accepted by the humanities and therein also modified, technology seems to remain fairly untouched by the revolutions that the idea of methodology have undergone. The technological bend of mind continues to adopt the method of statistics and verifiable data.
The Importance of Data in Scientific Method
In the previous article we had reflected upon the uses of data and standardizing of data in academic research. The problem of data is perhaps one of the crucial features which demarcates a scientific study from a non-scientific one. The sample, field or data that is used in the proposed study is crucial. Often due to a bad sample or inadequate data, the inferences of the study are incorrect. One of the ways in which the adequacy of data can be determined is by having a through selection of all possible material. While this is not always possible, automation and technological advancements can correct this error. Where human analysts can only analyze a limited number of documents within a given period of time, a similar automated system can accomplish a much greater number in the same time.
Structured information: The other advantage of automation is to provide data which is structured on the basis on a common parameter. This reduces the biases of the researcher and presents a uniformly and coherently structured data.
Random Sampling of Data
All documents/information on a given subject cannot be exhausted. Random Sampling is a well-known method of selecting data for analysis. The assumption of this method is that if a random sample from a given body of material is analyzed it is most likely to reflect the entire population/material. Random sampling
has however, recorded far too many misgivings, one of them being that the sample is actually not random enough. When it is selected by a human researcher, the chances of a design (based on intention) creep in.
There is no grounded method of science. It is only in the classification and analysis of data that we can attempt to find a certain scientificity. At the early stages of research, it is evident that automation can reduce several of the difficulties of conducting a study, making it more scientific and keeping a very little margin for unscientific elements to enter. It is at the second stage of research, the level of analysis that we will again reconsider the role of automation and technology in maintaining scientificity.