The Value and Limits of Science

A bit of background

On the recent Field and Field four-day course (June 8th – 11th 2019), Iain McGilchrist discussed key ideas from his book The Master and His Emissary. The Divided Brain and the Making of the Western World, talking for an hour on each. For the most part these talks were familiar as I have attended this course before.

  • Introduction to the Hemispheres
  • Brain Disorders of the Hemispheres
  • What is Language For?
  • Are we Becoming Machines?
  • What Does it Mean to Think?
  • The Power of No

I have blogged about these topics after attending previous courses.  See my page on The Divided Brain, on this blog.

But Iain is now writing a new book which will have the title (proposed, but not yet confirmed) – “The Matter With Things”. It was good to get this update, as on the last course I attended we were told that the title of the book would be There are no Things. I think Iain feels that his philosophical position is clearer with the newer title. This new book will argue against reductionism and materialism and for betweenness.

In the second part of this new book, which Iain is still working on, he will discuss what he told us are the four main paths to knowledge: science, reason, intuition and imagination. He stressed that we need all four, but that intuition and imagination have been downgraded in favour of science and reason, a result of left hemisphere dominance. So we were very fortunate to hear five one hour talks about these most recent ideas.

  • The Value and Limits of Science
  • The Value and Limits of Reason
  • The Values and Limits of Intuition
  • The Value and Limits of Imagination
  • Everything Flows

The value and limits of science  (These are the notes from Iain’s talk. Any errors are mine and I do not at all mind being corrected in the comments).

Collingwood wrote: Science and metaphysics are inextricably united, and stand or fall together.

And Heidegger wrote: Science does not think, science does not venture in the realm of philosophy. It is a realm, however, on which without her knowing it, she is dependent. (translated from the original by Iain McGilchrist)

(I cannot find these quotes online to verify them, and I learned on this course that my note-taking has slowed down, so I am not absolutely sure of their accuracy, but, as written, they provide the gist of Iain’s argument. For more on this, see the Update – 17-06-19 – at the end of this post.)

The word science simply means knowledge. We need science, but we rely too much on the left hemisphere. Public science is different to what good science is telling us.

The two hemispheres find two different worlds. Objectivity is not about what is out there. There isn’t a thing out there that we can know. Things only come into being through interaction with our consciousness. The more you dig into a tiny hole, the less you can see the whole. So the question is: What constitutes evidence in life? The ‘howness’ of the ‘what‘ matters a lot. Objectivity is a ‘howness’ – a disposition towards the world. You try to be just and truthful, to bring an understanding. This reminded me of the work of Gayle Letherby et al. on Objectivity and Subjectivity in Social Research .

There are no things that are not unique. How does science cope with this? In science when we say we understand something, we are comparing it to something else. Everything is built on analogy.

Science is not chaste (pure and virtuous). It starts from certain axioms/assumptions, e.g. the world is fully comprehensible physically. This is an unlikely but reasonable assumption. But why do we want to understand the physical?  Iain thinks this is related to ‘the matter with things’, the title of his new book, so I expect we will learn more about this when the new book is published (hopefully by the end of 2020).

Science is reluctant to accept anything that can’t be measured. It is based on a false dichotomy between facts and value. There is always a value involved in seeking any kind of truth. We try to rise to meet this through objectivity. Many things in science can’t be separated from value, but there is value involved in appreciating what is a fact.

Problems with science

There are 3 problems with science

  • Intrinsic problems built on assumptions
  • Problems of the model of the machine
  • Institutional problems – the way science promulgates what it is doing

Intrinsic problems built on assumptions

There is no one truth, only more or less truth, but we must be loyal and faithful to truth. (See Where Can we go for Truth? for more of Iain’s thoughts on truth). So how do we decide which questions are worth asking?

Values, judgement and insights are very important in science. Great scientists allow ideas to incubate for a long time. Science eliminates the idea of purpose. This is a tenet of science; there is no purpose to science. Science cannot address things like love or an understanding of God. We can see these in operation, but they cannot be explained by science. But science is teleological – things happen for a reason, although the value of reason itself can’t be reasoned.

An example of a problem built on assumptions is DNA. DNA is not a building block; there is just not enough information in DNA. DNA is a resource from which the cell can draw. It is not a script. Only 2% of it expresses anything. Quarter of a million new neurones a minute are developed in the brain. We cannot get this from a linear script. The genome is not the answer.

Problems of the model of the machine

We are not machines. A machine can be switched off, but life is constant and cannot be switched off. A machine operates close to equilibrium; you have to put energy in to make it change. Life is the exact opposite. It is always changing, but how does it remain stable enough to keep going better? Through homeostasis. Human beings and living things change. Natural selection is the thing that stops change, it doesn’t cause change.

Organisms are not on/off. They involve inconceivably complex reactions to maintain stability between motion and stasis. They are non-linear, action is not one-way as in machines. The parts of organisms themselves are changing. This doesn’t happen in machines. The genome restructures itself all the time. DNA is not the robot master. The same genes can give rise to different effects, e.g. Pax6 gives rise to different eyes in the fly, the frog and humans. Some animals can regenerate parts of their body. If you cut off the head of a nematode worm, it will grow a new head with the same memories. Living organisms are not machines. The instructions for life are within the organism.

See also a previous post – The Human Versus the Machine 

Institutional problems

Science is carried out by normal people with egos etc. Fashions of thinking dominate. Science depends on results, safety, conformity, narrowness. There are many dogmas that can’t be broken.

Scientists are expected to publish or perish. This is destructive to morale. Scientists are rated on the number of papers they can churn out, but they need fallow periods, and they can get caught up in administration, particularly if they get promoted.

Lots of science papers need to be retracted, because they have been made up. And Ceci and Peters’ research raised doubts about the reliability of the peer- review process.

Scientists are also subject to predatory journals to the extent that Jeremy Beale published a list of journals which researchers should avoid.

Truth matters, but these problems with science show that finding out what is true is more difficult. We need more replication work. The amount of replication work is very low.

Why is truth important? We are here to engage with the world. If it is pointless why go with truth?

Update (17-06-19) re the Heidegger quotes (with thanks to Iain McGilchrist for this information)

The first part, » Die Wissenschaft denkt nicht «, is originally from page 4 of Heidegger’s Was heißt denken?, the version of his lectures given in Freiburg in 1951-2 published by Max Niemeyer Verlag, Tübingen (1954), and later translated into English by FD Wick & JG Gray as What is Called Thinking?(Harper & Row, 1968).  Heidegger then repeated it in a conversation with his pupil the German philosopher Richard Wisser on the 17th September 1969, in which he follows it by another phrase in explanation, thus: » Und dieser Satz: die Wissenschaft denkt nicht, der viel Aufsehen erregte, als ich ihn in einer Freiburger Vorlesung aussprach, bedeutet: Die Wissenschaft bewegt sich nicht in der Dimension der Philosophie. Sie ist aber, ohne daß sie es weiß, auf diese Dimension angewiesen «. In H Heidegger (ed), Martin Heidegger: Gesamtausgabe, Part One, Veröffentlichte Schriften 1910-1976, vol 16, Reden und Andere Zeugnisse eines Lebensweges, Vittorio Klostermann, Frankfurt am Main, 2000, 702-710 (705).

#SOCRMx Week 7: Objectivity and Subjectivity in Social Research

The past two weeks in the Introduction to Social Research Methods MOOC (SOCRMx) have covered ‘How to Lie with Statistics’ (Week 6 Quantitative data analysis) and ‘Interpretation and Trustworthiness in Qualitative Data Analysis’ (Week 7).

Lots of resources have been provided for the Week 6 topic on quantitative data analysis. I have saved them for future reference, should I ever go down that track. It was interesting to see that more course participants got involved in discussion in the Week 6 forums. I’m not sure if that’s because a positivist approach comes more easily to those new to research, or whether it was because once again the resources were extremely good and the tutor for this week – Rory Ewins – actually engaged in the forums and provided feedback!

The resources for Week 7 on qualitative data analysis are very familiar given that all my research has taken this approach, but once more the resources provided on what we mean by trustworthiness and how to code qualitative data are helpful and thought provoking. Again I have saved these resources for future reference when I can explore them with more time. I think both these topics require and deserve more time than one week to get to grips with.

The topic of qualitative analysis reminded me of an excellent video that was shown in the first week of the course under the title of theoretical considerations in research design. This is a SAGE research methods video and therefore not open access, but if anyone from SAGE visits this post, I would urge them to make this video publicly available. I think both novice and experienced researchers would find it hugely helpful.

The video shows John Scott (Professor of Sociology, Plymouth University), Malcolm  Williams (Professor in the School of Social Sciences, Cardiff University) and Gayle Letherby (Professor of Sociology , Plymouth University) discussing objectivity and subjectivity in social research, having published a book about this in 2014:

Scott, J, Williams, M & Letherby, G. (2014). Objectivity and subjectivity in social research, SAGE Publications Ltd., London.

These are the notes I made from watching the video.

John Scott starts by drawing on the work of Kant and Mannheim to discuss how subjectivity, objectivity, relativity and truth have multi-faceted meanings. We see the world differently according to our social location (our perspective) and we construct knowledge relative to that location. But if this is so, how, as researchers, can we present a truthful representation of the world. One way we can do this (which Iain McGilchrist has also discussed in his work on the Divided Brain) is to try and synthesise different standpoints (alternative perspectives) to achieve a better model for understanding the world.

The three authors each approach research from different perspectives. Malcolm Williams adopts a socially situated objectivity approach, Gayle Letherby a theorised subjectivity approach and John Scott, is somewhere in the middle of these two perspectives, and the point was made that it’s not necessarily a question of either/or, as shown by mixed methods approaches.

Malcolm Williams sees objectivity as a value which is socially constructed. The pursuit of truth is a key research value and he argues that a necessary condition of objectivity is to begin with subjectivity.

Gayle Letherby recognises the personhood of research and the complex relationship between researcher and respondent. From this perspective research is subjective, power laden, emotional and embodied. A theorised subjectivity approach is concerned with how identity plays out in the research process. Gayle Letherby tells us that interrogation of the self in research, with reference to the ‘other’, gets us closer to a position that we might call objective and that autobiography is always relevant. We need to interrogate our biases, but we should avoid demonization of subjectivity. Subjectivity is not a redefinition of objectivity, but starts from a different place. Objectivity and subjectivity are interrelated.

Whether we take a subjective or objective approach, social science research does have validity and needs to be defended. This does not mean that one account is as good as any other. Researchers must be responsible and explain how we got what we got and how what we did affects what we got. Research always involves a view from somewhere and we need to write about our subjective positions. Where did the research question come from? Why was a particular approach adopted? Can we justify why our findings are important? What is our ethical position? Have we acknowledged that ideas about knowledge can’t be separated from ideas about power? (Foucault’s work is relevant here) The social and political role of research means that there is no escape from issues of power.

Scott, Williams and Letherby conclude that the validity of knowledge depends on having:

  • Open societies (Karl Popper)
  • An ideal speech situation (Habermas)
  • Free discussion (Mannheim)
  • Leaving power outside the door as much as possible.