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As my field, Computer Science (CS), has many sub-areas and specializations. I found myself having a not-so-good impression about different areas within CS. For example, I see working on Software Engineering as a waste of time while working on Artificial Intelligence (AI) is much more worthing an investigation.

This is not a field-specific question, I wish hearing whether this exists on other fields as well. Is it common in academia for individuals to find some subfields of a more broader research area more interesting and relevant than others? Relatedly, how does one avoid thinking that way?

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On a somewhat related note, my research went from "soft science" to "hard science" just by changing department. I think both terms are used in a slightly derogatory way. –  StrongBad Jan 22 '13 at 17:33
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Just because you think it is worthless, does not make it so. My own experience is that this kind of thoughts stem more from personal predjudice and interest than from objective fact. –  Paul Hiemstra Jan 22 '13 at 20:17
    
This is actually a good thing (TM). If you don't feel that way about your field, then is it really your field? –  emory Jan 22 '13 at 23:01
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Although there is a difference between loving your own field, and disliking others. I think it is possible to love what you like to do without being condescending about what other people like to do. –  Paul Hiemstra Jan 22 '13 at 23:38
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Relevant: youtube.com/watch?v=0la5DBtOVNI –  JeffE Jan 24 '13 at 4:06
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5 Answers

up vote 12 down vote accepted

Just like the grass is always greener on the other side, it is somewhat usual for people to think that their particular area of expertise is somewhat more valuable than others. I have seen it in all fields I have heard of…

And it's not restricted to academic life: I'm sure the cardiac surgeon feels that his work is so much more important than that of the family physician, while the later thinks that he's the one tasked with stopping epidemics and diagnosing the important stuff to save lives.

But I'm sure you already knew that… so, what is your question exactly?


Edit: how does one avoid thinking that way?

The most difficult part, for me, in thinking objectively about other fields is to be able to correctly identifying the challenges they face. I find that it is altogether too easy to think “hey, that's a trivial optimization problem” or “know that they know the compound formula, I wonder why it takes them so long to synthesize it”. Better overall understanding of other fields helps a lot avoid this way of thinking. It takes a lot of effort to acquire such a broad general knowledge: reading, listening to conferences outside your area of expertise, chatting with colleagues, …

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I think the "How does one avoid thinking that way?" part is key. –  StrongBad Jan 22 '13 at 18:18
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It's important to distinguish "what I find interesting" from "what someone finds interesting" from "what I think is important" from "what is important to others".

It is perfectly acceptable to find other fields uninteresting - that's just the nature of subjectivity and personal preference. Or more poetically, "vive la difference".

It is less acceptable to go from "I personally find this area boring" to "this area is boring". At that point, as others have suggested, you need to understand why others in the field find it interesting. Ask them ! Put yourself in their shoes. (I will often say, "I'm glad someone is working in area X and I'm glad it's not me" :).

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The harder prejudice to avoid, at least for me, is not "this field is boring" but "work in this field is poor science, even on its own terms". –  JeffE Jan 23 '13 at 3:20
    
@JeffE: but that is already a more objective statement. Just to note, in some fields, the nature of the tackled problem is such (at the current state of the art), that we do not have a good grasp on its context (yet). Work on such problems then often seems to be "substandard craft". Compare e.g., early research on knowledge representation with hard stuff produced later on. And the same goes for e.g., image recognition, etc. –  walkmanyi Jan 23 '13 at 9:52
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The same thing happens in many different fields, mathematics, physics, etc... Many in academia criticize computational scientists like myself as too interdisciplinary (i.e. we are jack of all trades, but masters of none).

I think it naturally emerges from the competitive nature of academia these days. We're all competing for precious resources, such as funding, tenure track positions, prestige and attention, etc. To be competitive, we must assert that our work is more worthwhile than others. In the face of criticism and competition, people of the same field sometimes are tempted to assert their worth by belittling the value of other fields.

There's no better way to avoid doing this than to distance yourself from others who engage in the "we're better than them" attitude. Unfortunately, it can be difficult if you're already surrounded by people with this mentality already. In which case, it might be worthwhile to just get out of your comfort zone and attend research seminars and presentations in completely unrelated fields. Sometimes, if you surround yourself with others who value a topic that you don't, you can learn how to appreciate it in ways you've never thought of before.

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Is valuing one field over another is a common behavior in academia?

The other answers clearly answer yes. There can be subjective reasons to such an observation (e.g., a cardiologist could feel more superior to a gastroenterolog), but there might be also an objective part to the observation (as you example goes, the results produced in software engineering are somewhat shakier than those in graph theory).

How does one avoid thinking that way?

Besides an excellent point by other answer saying "you should try to better understand the challenges of the other field", I also argue that you should better understand the dynamics of scientific pursuit in general.

Kuhn, in the Structure of Scientific Revolutions argues that scientific work in any given field has three phases. The first, pre-paradigm and subsequent transition to normal science are relevant for this answer:

The first phase, which exists only once, is the pre-paradigm phase, in which there is no consensus on any particular theory, though the research being carried out can be considered scientific in nature. This phase is characterized by several incompatible and incomplete theories. If the actors in the pre-paradigm community eventually gravitate to one of these conceptual frameworks and ultimately to a widespread consensus on the appropriate choice of methods, terminology and on the kinds of experiment that are likely to contribute to increased insights, then the second phase, normal science, begins, in which puzzles are solved within the context of the dominant paradigm. Etc.

Often we observe somewhat substandard results and works in fields which clearly fall into the category of those still being in the pre-paradigm phase. Your specific question is relevant to this due to the fact, that whole of computer science is still a young field and many problems we are solving are new, often vague, or ill defined, etc. This is is especially the case for the fields and communities tackling applications of applied-mathematics-style computer science to real-world applications, i.e., software engineering. Your reference to software engineering is clearly the case here, large parts of artificial intelligence fall into this category as well, and I am sure other fields and subfields too.

Even if you find yourself working in a "soft" field, it does not necessarily mean the niche community is not tackling a sound problem (though sometimes it is the case, but you need to look very carefully into it). Sometimes working on such can be even more demanding/challenging/satisfying than routinely solving puzzles in the normal-science context.

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I think a part of being a human is assigning different values for different things (or preferring, or choosing something among other things). So I do not worry about valuing some field of study more than others.

I think the real challenge is to be realistic. I mean one should know his abilities, his interests and find a (scientifically rigorous) field which matches with his abilities and interests.

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