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).

Publishing in open access journals

Periodically I receive a message from Taylor and Francis about how often a paper I published with Mariana Funes has been read. This week they sent me the following message:

Of course Taylor and Francis can’t know whether or not the article has been read. They can only know how many time the article has been clicked on or downloaded. And, yes, sharing the article on social media (as we did when it was first published on Feb 28 2018) may well increase the article’s reach.

It is gratifying to see that the article has been accessed more than 500 times on the Taylor and Francis website, but of course Learning, Media and Technology is a closed journal so the reach of the article will necessarily be confined to those with access.

But the journal did allow for open publication of the pre-print of the article, which Mariana and I did on this blog, where we provide open access to the pre-publication (but virtually identical) article for free. This version of the article has had a much wider reach.

In 2018, this blog post was clicked on 4,070 times. This year to date it has been clicked on 231 times.

Again, it is not possible to say whether or not the article has been read, only that there is sufficient interest in it for people to click on the blog post.

As yet, there hasn’t been a mad rush to cite this paper. Google Scholar shows that it has been cited twice this year. Whilst it may be that this is not a paper that will be much cited, I do know from experience that it can take a year or two for papers to come to the attention of other researchers, so there is time yet for it to be more widely cited.

It is possible that open journals still don’t have the kudos of closed journals. Someone recently told me that it wouldn’t be worth my while applying for a job that required a PhD and 4 papers published in ranked journals, because most of my papers have been published in open journals, which, because many are fairly new journals, are still building their reputation. It has always been my preference to publish in open journals. I appreciate Taylor and Frances wanting their journal articles to reach a wider audience, but I suspect that their reasons for this are different to mine.

Kudos or not, the point is that it is not simply using social media to disseminate research that makes a difference to its reach. My experience suggests that extending a paper’s reach depends at least in part on whether or not the paper can be openly accessed.

But I am grateful to the Learning, Media and Technology journal for not only publishing our article, but also allowing us to openly publish the final pre-print version, and I will now follow their advice and tweet this blog post, so that, hopefully, the article will continue to reach a wider audience. The prize for every author is to be read.

Reference

Funes, M. & Mackness, J. (2018): When inclusion excludes: a counter narrative of open online education, Learning, Media and Technology, DOI: 10.1080/17439884.2018.1444638 When Inclusion Excludes MF:JM 280218