- Four stages of a scientific discipline; four types of scientist
By Alexander M. Shneider
Trends in Biochemical Sciences
Volume 34, Issue 5, May 2009, Pages 217-223
In this paper, Shneider suggests to distinguish four different stages of a scientific discipline, that I will briefly summarize below. It is somewhat ironic the author writes he believes "this analysis could be instrumental for individual researchers in their career planning," but then publishes his paper in a subscription journal that wants to charge you US $ 31.50 for a 6 page article.
The identification of different stages through which a research program goes is an approach that resonates with me. I have been previously referring to this vaguely as "the stage of creative process," and have pointed out many times that there can be no overall prescription for what amount of "transformative" and "conservative" research a discipline needs that does not take into account different fields are in different creative phases. Thus any call for more support of one or the other research style is an oversimplified panacea that might or might not work in one or the other case.
However, though Shneider's paper is interesting, I don't find it very well thought through. In particular, the author tends to speak of the characteristics of a "science" and of "scientists," though I doubt these ever occur in a pure form. It would be more useful to characterize a specific research project, and then identify the stage of the discipline by what sort of projects are mainly pursued, or similarly characterize a scientist by what sort of projects he mainly works on. You also wouldn't call a restaurant or its cook "spicy," you'd call a dish spicy and then say the restaurant offers many spicy dishes, and the cook is known for them.
In any case, here the characteristics of the four stages:
- Introduction of new subject matter
- New scientific language
- Often based on new observations and/or experimental results
- First stage scientists not necessarily the ones who discover new facts
- First stage scientists often need to be somewhat imprecise or inaccurate because not all necessary facts are known or properly comprehended
- Theory often contains uncertainty
- First stage scientists do not always possess exquisite technical skills.
- Philosophical, aesthetic and cultural views, analogies and literature are instrumental to the first stage scientists' mode of thinking
- Development of major techniques
- Often re-applications of methods previously developed in another discipline (plus rethinking and adjustments to new task)
- Main characteristic of second stage scientists are ingenuity and inventiveness, an ability to implement ideas and a high risk-tolerance
- Most of the actual data and useful knowledge is generated
- Re-description of subject matter, creation of new insights and questions
- Difficulties and unexplained phenomena often give birth to new first stage
- Most useful personal qualities of third stage scientists are detail oriented, neat, hard working
- Extensive knowledge of philosophy or art is not instrumental
- Communication and carrying on of knowledge
- Reviews, organization of knowledge
- Without the fourth stage scientists, the explosion of new data generated at the third stage would be chaotic
- Development of applications
- Re-evaluation of the role of the discipline in a possibly changing social and cultural context
- Forth stage scientists use a broad spectrum of cultural and philosophical views
- Forth stage work serves to inspire new generations of scientists
The paper also has some remarks on how these four stages relate to Kuhn's theory of scientific revolution. The author points out that Kuhn was aiming at characterizing paradigm shifts, not the life-cycle of scientific disciplines.
I think one should consider that a discipline might run into a case of arrested development in any stage, in which case too much effort goes into the wrong research direction. Unfortunately, such cases might become self-supporting due to the present organization of the academic system in which people go where money goes, and money goes where people go. This leads to the formation of the scientific analog to economic bubbles. As a result, the amount of people working in a field does not accurately reflect its actual promise.
Shneider provides many examples, but these are dominantly from past centuries and from biology and chemistry. If you have a current example, leave it in the comments.