Perhaps one of the reasons for vaccination hesitancy is that we are too casual about what constitutes science.

Some Māori health providers recently speculated that misinformation spread via social media is partly to blame for the poor vaccination rates among Māori (below the age of 40 – it is about two-thirds of the Pakeha rate). (In contrast Asians and Pasifika rates tend to be ahead the Pakeha ones.) Presumably the Māori providers, nearer the ground than I, have detected vaccine hesitancy.

Māori are not unique. Vaccine hesitancy and a lack of trust in vaccine science is more widespread than one might expect, given the evidence overwhelmingly favouring vaccination as a way of markedly reducing the threat of the Covid virus to the individual and to the wider community in which he or she lives. (I like the image that not vaccinating is like leaving your lights on in a blitz. It not only made you a better target for the bombers, but threatened your neighbours.)

One of the reasons that people may not trust the science is the way science has been behaving. That is a reasonable inference from Stuart Ritchie’s Science Fictions which records how poor quality science undermines the standards scientists set for themselves.

Ritchie is a psychologist but his detailed examples range across medicine, physics, nutrition, education, genetics, economics, and the search for extraterrestrial life. He shows how the increasing commercial pressures on science have led to a form of organisation which corrupts the spirit of the enterprise. The resulting fraud, bias, negligence, and hype undermines public confidence in science. That may well spill over into unnecessary scepticism about the efficacy of vaccinations.

‘Fraud’ by a scientist is always a possibility as it is elsewhere in human life. Ritchie gives some uncomfortable examples, although pure fraud is not that common.

Much more common is ‘bias’ where the researcher has a particular view and reworks the results to confirm the view, instead of starting off with a view and trying to reject it, only accept the hypothesis when all other possibilities prove to be less plausible explanations. (And if one finds a better explanation ...WOW!)

Many readers will find challenging (as Ritchie acknowledges) the centre of the chapter on bias in which he explains statistical testing. If so, skip pages 90 to 112. That means you wont have ‘p-hacking’ explained. When later in the book, the term is used, substitute the economists’ term ‘data mining’, which is manipulating the data until the researcher gets the desired answer: ‘the data is tortured until it confesses’.

Negligence is when scientists make unintentional errors – I would prefer to call it ‘mistakes’. We all make them, but we usually self-correct. Some slip through the net. Ritchie suggests the science professions are not always good at picking such errors up because crosschecking others’ work is not encouraged.

Fraud, bias and negligence are all magnified by hype, when the research goes into the public domain and the typically limited research is twisted to draw conclusions which are not justified. Ritchie gives some extraordinary examples too detailed to report here. But there are patterns like ‘unwarranted advice’ (conclusions that the study does not support); the ‘cross-species leap’ (generalising from a species studies, such as on mice, to humans – 90 percent of the mice findings do not generalise); inferring ‘causation from correlation’ (a nice example I recently came across is that regions with more tractors have higher rates of colorectal cancer – they both occur in rural areas).

He does not deign to mention a common New Zealand approach of ‘not having a contrast’ such as reporting that X percent of group A (which the investigator usually belongs to) suffers condition N without mentioning the rate for group non-A or other subgroups (which might be the same).

And there is a widespread ‘misunderstanding of sampling theory’. It requires random samples; non-random samples generate biased results. A nice recent example of this ignorance was the criticism of Pasifika having high Covid case rates. But the cases are not from a random sample – you catch it in your community – but the clusters might be. (I celebrate that the Pasifika are strong community peoples; it is unfortunate that such communities makes it easier for Covid to spread).

Hyping is nicely captured in a recent cover story of The Listener: ‘Under the Lid: Top Harvard professor on smarter thinking’. The use of ‘top’ is a dead giveaway; the story is for the credulous. It is either going to be banal or draw stronger conclusions than the (probably quality) research justifies. The same unwarranted advice happens in economics too, when the commentator makes a conclusion which is outside the available evidence but which often – as you wont be surprised – reflects their politics. Journalism has devalued the term ‘expert’ to mean somebody with status and an opinion.

Note too, the reference in the cover story to a prestigious university. I was struck by how often such institutions appeared among Ritchie’s examples, probably because they have so many researchers but it is compounds our credulity. (Ritchie has examples of institutions defending a poor quality researcher when they should have known better.)

Prestige is often key to understanding fictional science. Consider the recent controversy over whether that mātauranga Māori was a science. The issue hinges on the meaning of ‘science’, the German term for which is ‘Wissenschaft’. But the word also has a broader meaning – the systematic pursuit of knowledge, learning, and scholarship (especially as contrasted with its application). That includes a lot of knowledge which is not ‘science’ as the term is usually used – including literature (a type of knowledge which, as my regular readers well know, I greatly respect).

Science can be characterised by the (Robert) Merton criteria:

            universalism: scientific validity is independent of the sociopolitical status/personal attributes of its participants;

            disinterestedness: scientific institutions act for the benefit of a common scientific enterprise, rather than for the personal gain of individuals within them;

            communality: all scientists should have common ownership of scientific goods (intellectual property), to promote collective collaboration; secrecy is the opposite of this norm;

            organised scepticism: scientific claims should be exposed to critical scrutiny before being accepted: both in methodology and institutional codes of conduct.

Does mātauranga Māori want to meet these criteria? Would it be happy with the logic that all scientific knowledge is tentative? One of our greatest scientists was Isaac Newton, but a century ago his account of the universe was overturned by Albert Einstein. Today, there is evidence that Einstein’s theory is also problematic but scientists have yet to work out an alternative to replace it.

Mātauranga Māori seems to be like religious truth with its focus on eternal verities. It would be terrible if being overly rigid about Māori science limited some young Māori going into scientific careers; one day one may be awarded a Nobel Prize. There are already some very good Māori scientists (including one who signed the Auckland critique). I cannot speak for them but I should not be surprised if they respect mātauranga Māori but keep it in a different part of their being (just as many non-Māori scientists hold their religious beliefs).

The other worry is that confusing mātauranga Māori with science confuses the ordinary Māori which leads to the vaccine hesitancy which concerned those Māori public health providers. They should take a leaf from their Pasifika cousins who hold to their religious verities but who get vaccinated.

Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth by Stuart Ritchie