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The Tao of plagiarism: when chi achieves enlightenment without you

by a mildly surprised emeritus professor

Academics are taught many things over the years. How to write grant applications in a tone of sober optimism. How to disagree politely while eviscerating an argument. How to pretend that Reviewer 2’s comments are ‘helpful’. But we are rarely prepared for the moment when our own work achieves enlightenment and returns to the world under a different name.

It began, as these things often do, with Google Scholar. Browsing innocently, I discovered that a paper I had written many years ago, first author with two colleagues, had been reborn. Here it was miraculously renewed: Freeing the chi of change: The Higher Education Academy and enhancing teaching and learning in higher education. Same title. Same argument. Same metaphors. Different authors. Different journal. Different universe.

This was not mere influence. Nor was it scholarly dialogue. This was something more metaphysical. The article had apparently passed through the cycle of samsara, shedding its original authorship like an old skin, and had re-emerged – serene, confident, and wholly unburdened by attribution.

Opening the paper produced a strange sense of déjà vu. Paragraphs unfolded exactly as I remembered writing them. The argument progressed through familiar analytical levels. The meso level was, once again, mysteriously absent. And there it was: the metaphor of chi – blocked, stagnant, yearning to be freed – flowing unimpeded across two decades and several thousand miles.

One could not help but admire the fidelity. This was not slapdash copying. This was careful stewardship. A lightly paraphrased abstract here, a synonym substituted there. “Examines” had matured into “takes a look at”. “Work intensification” had achieved inner peace as “an increase in workload”. The original prose had been gently guided toward a simpler, more mindful state.

The production values added to the sense of cosmic theatre. Running headers attributed the article to someone else entirely, suggesting either deep enlightenment or mild confusion. Words occasionally developed spontaneous internal spacing, or none at all, as if even the typography were observing a vow of non-attachment. Peer review, meanwhile, appeared to have transcended physical form altogether.

At moments like this, one is tempted to ask philosophical questions. What is authorship, really? If an argument is copied perfectly, does it still belong to its original creator? If a journal publishes without editors in the room to hear it, does it still make a sound? If a metaphor about blocked chi appears in the forest of academic publishing, does anyone notice?

And then there is Google Scholar, calmly indexing it all, like a Zen monk sweeping leaves while entire epistemologies collapse around him.

The emotional journey is predictable. Surprise gives way to irritation, which in turn yields to a kind of exhausted amusement. After all, it is not every day one gets to read one’s own work as if it were new – especially when it has been thoughtfully simplified for contemporary consumption.

Correspondence followed. Screenshots were taken. Appendices multiplied. Examples of verbatim overlap were laid out with the careful precision of a tea ceremony. The original article was cited. The reincarnated article was cited. Karma, it seemed, was being documented.

What lingers after the initial absurdity is not just concern about misconduct, but about the ecosystems that allow such reincarnations to flourish. Journals without editors. Publishers without addresses. Ethics policies without enforcement. A publishing landscape in which the appearance of scholarship is often sufficient, and coherence is optional.

Perhaps this is the true lesson of Eastern philosophy for higher education. When systems lose balance, chi stagnates. When oversight weakens, energies flow in unexpected directions. When scholarly publishing detaches from accountability, articles achieve nirvana without the inconvenience of authorship.

The good news is that the chi remains remarkably resilient. Even when blocked, it finds a way. It circulates. It reincarnates. It reappears – sometimes with better spacing, sometimes with worse.

As for me, I have learned a valuable lesson. Should I ever wish to republish my earlier work, there are evidently paths that require no revision, no peer review, and very little effort. I will not be taking them. But it is oddly comforting to know that my chi, at least, is doing well.

SRHE Fellow Paul Trowler is Emeritus Professor of Higher Education at Lancaster University. His work focuses on teaching, learning, and organisational change, with a long-standing interest in how academic practices operate in everyday settings. More recently, he has been working on doctoral education and the practical use of AI within learning architectures that support research and learning. He continues to write and develop tools that emphasise dialogic, theory-informed approaches rather than transmission-led models.


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Educational expertise in a post-truth society

by Richard Davies

The inauguration ceremony for Donald Trump was interesting to watch for several reasons, but the Battle Hymn of the Republic caught my ear. Whilst the song has cultural connections for Americans, its explicit religiosity and commitment to truth seems at odds with modern sensibilities. Rather than truth, recent political history, eg Johnson, Trump, Brexit and Covid-19 (anti)vaccination, has shone a light on our post-truth society, where, as Illing (2018) notes, there is a disappearance of ‘shared standards of truth’. In such a society politics shifts from being the discussion of ideas or even ‘what works’ to a play for the emotions of the majority. A context within which Michael Gove, an early adopter, was able to label a raft of educational luminaries ‘the blob’ (see Garner, 2014).

Whilst this is/might be irritating and socially disabling, I want to argue that it is also both deleterious to educational research and that its roots lie some 250 years ago.

Pring (2015) argued that what makes educational research distinctly educational is its intention to improve educational practice. So, research about education is not sufficient to qualify as  educational research; educational research intends to change educational practice for the good of learners (and often wider society). This requires several activities including shared dialogues between researchers, practitioners and other stakeholders with common ways of talking about education and common standards of truth (see Davies, 2016). An environment of post-truth undermines such possibilities, as I hope will become clearer as I explore the roots of the present malaise.

The roots lie around 1744 or just before, signalled by Vico’s New Science, or at least in the 18th century, where MacIntyre (1987) places the last foothold of the ‘educated public’ – and it is in MacIntyre that I ground the argument here. MacIntyre (1985) presents a historically informed account of the decline of ethical discourse and, on a more positive note, what is required for its restoration. Here I fillet that account for the resources I need for my purposes (see Davies, 2003, Davies, 2013 for more detailed reviews). He argues that ethical discourse has undergone a series of transformations, led by philosophers but now part of the public zeitgeist, causing a situation in which people believed there was no reasonable basis on which to resolve ethical disagreements.

Here, I identify just three key elements of the argument. Firstly, naïve relativism, the (false) view that because people disagree on a matter then, necessarily, there must be no rational means to resolve the disagreement. Secondly, MacIntyre identifies three, non-rational approaches to decision making: (i) personal taste, (ii) achieving the goals of the system of which one is a part, or (iii) through interpersonal agreement. These are embedded, MacIntyre claims, in our social activities and institutions. Thirdly, that these give rise to a distinctive form of political engagement, protest. In protest different sides shout their differing views at each other knowing both that their views will not change the views of their opponents nor that their opponents’ views will change their views.

When we see ‘toddler’ behaviour from politicians, it is a focus on personal taste and the tantrums that emerge when these are frustrated. What reasons, they might say, do others have to frustrate what I want, for no such reasons can exist. When we see claims that the democratic process must be followed, we are seeing a commitment to achieving the goals of the system; what else can be done? We regularly see examples of protest, often mistakenly seen as ‘facing down’ a critique of one’s behaviour. The views of others only count if they have some reasons for their views that might be better than mine. But for those embracing the obviousness of naïve relativism this cannot happen, rather protests (against Johnson, Trump, and others) are just attempts to make them feel bad. Such attempts must be resisted through and because of bravado.

How do the politician and policymaker operate in such an environment? Bauman (2000) offers a couple of practical conceptions consistent with MacIntyre’s critique. Firstly, Bauman draws attention to the effect of having no rational basis for decision making: it is increasingly difficult to aggregate individual desires into political coherent movements. Traditional political groupings on class, gender and race are dissolving (which is certainly a feature of the 2024 US election analysis). It matters less why you want to achieve something; it is just that we can have interpersonal agreement on what we claim we want to achieve. Secondly, Bauman talks of decision making as reflecting the ‘script of shopping’, we buy into things – friendship groups, lifestyles, etc – and as suddenly no longer do so when they do not satisfy our personal desires. Whilst this may seem overly pessimistic, Bauman and MacIntyre are identifying the unavoidable direction of human societies towards this already emergent conclusion.

Politicians and policymakers play, therefore, in this world of seeking sufficient co-operation to build a political base – to get elected and to get policies through. They do this by getting individuals to buy into the value of specific outcomes (or more often to stop other awful outcomes). They are not interested why individuals buy in, nor do they try to develop a broader consensus. There are no rational foundations, and any persuasive tactic will do, with different tactics deployed to influence different people. This scattergun approach is more likely to hit the personal desires of the maximum number of people.

Where does this leave the educational researcher seeking to influence educational policy and practice based on their research endeavours? At best, we might become the chosen instrument of a policymaker to persuade others – but only if our research agrees with their pre-existing desires. Truth is not the desired feature, just the ability to be persuasive.

But what if truth does matter, and we want to take seriously our moral responsibilities to support educational endeavours that are in the interests of students? There are four things we can do.

  1. Understand the situation. It is not just that the political environment is hostile to research, it does not see facts as a feature of policy and practice development.
  2. Decide if we want to be educational researchers or policymakers. The former means potentially less engagement, impact, and status, perhaps walking away from policymaking as more ethically defensible than staying to persuade using simulacra of evidence.
  3. Get our own house in order. We have too many conferences which provide too little time to discuss fundamental differences between researchers, with so many papers that we are only speaking to people with whom we more or less agree. The debates are over minutiae rather than significant differences. Dissenting voices tend to go elsewhere and move on to different foci rather than try and get a foot in the door. Bluntly, our academic system is already shaped by the same post-truth structures that have given rise to Trump, Johnson, et al (and no doubt most of us could identify our equivalents of them). Although we will never speak with one voice and will, I hope, always embrace fallibility, getting the house in order will enable us to model what rational dialogue and truth seeking can achieve in identifying how educational policy and practice can be enhanced. Of course, we should value each other’s contributions, but not confuse value with valid (it is just another form of naïve relativism).
  4. Find some allies who accept a similar account of the decline of reason from amongst politicians and policymakers and work out how we start to make educational research not only relevant but influential.

Richard Davies leads the MA Education Framework programmes at the University of Hertfordshire. His research interests include philosophical issues in higher education. He is a co-convenor of the Academic Practice Network at SRHE.


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The Doorknob and the Door(s): Why Bayes Matters Now

By Paul Alper

When I was young, there was a sort of funny story about someone who invented the doorknob but died young and poor because the door had yet to be invented. And, perhaps the imagery is backwards in that the door existed but was useless until the doorknob came into being but I will stick with the doorknob coming first in time. Bear with me as I attempt to show the relevance of this to the current meteoric rise of Bayesianism, a philosophy and concept several centuries old. 

In a previous posting, “Statistical Illogic: the fallacy of Jacob Bernoulli and others,” I reviewed the book, Bernoulli’s Fallacy by Aubrey Clayton.  He shows in great detail how easy it is to confuse what we really should want

Prob(Hypothesis| Evidence)                                       Bayesianism

with

Prob(Evidence | Hypothesis)                                       Frequentism

A classic instance of Bayesian revision in higher education would be the famous example at the Berkeley campus of the University of California. In the 1970s, it was alleged that there was discrimination against females applying to graduate school.  Indeed, male admission rate overall was higher than female admission rate.  But, according to https://www.refsmmat.com/posts/2016-05-08-simpsons-paradox-berkeley.html, the simple explanation

“is that women tended to apply to the departments that are the hardest to get into, and men tended to apply to departments that were easier to get into. (Humanities departments tended to have less research funding to support graduate students, while science and engineer departments were awash with money.) So women were rejected more than men. Presumably, the bias wasn’t at Berkeley but earlier in women’s education, when other biases led them to different fields of study than men.”

Clayton’s examples, such as the Prosecutor’s Fallacy and medical testing confusion, give no hint of how analytically difficult it was to perform the calculations of Bayes Theorem in complicated situations. Except for a paragraph or two on pages 297 and 298 he makes no reference to how and why Bayesianism calculations can now be done numerically on very complicated, important, real-life problems in physics, statistics, machine learning, and in many other fields, thus the proliferation of Bayesianism.

For the record, the one and only picture of the reverend Thomas Bayes is generally considered apocryphal; ditto regarding the one and only picture of Shakespeare.   Bayes died in 1761 and his eponymous theorem was presented to The Royal Society in 1763 by Richard Price, an interesting character on his own.

What has changed since the inception of Bayes Theorem more than two centuries ago, the door knob if you will, is the advent of the door: World War II and the computer. At Los Alamos, New Mexico, the place that gave us the atom bomb, five people were confronted with a complicated problem in physics and they came up with a numerical way of solving Bayes Theorem via an approach known as MCMC, which stands for Markov Chain Monte Carlo. Their particular numerical way of doing things is referred to as the “Metropolis Algorithm” named after Nicholas Metropolis, the individual whose name was alphabetically first.

To give the flavour but not the details of the Metropolis algorithm, I will use a well-done, simple example I found on the web which does not use or need the Metropolis algorithm but can be solved simply using straightforward Bayes Theorem; then I show an inferior numerical technique before indicating how Metropolis would do it. The simple illustrative example is taken from the excellent web video, Bayes theorem, the geometry of changing beliefs (which in turn is taken from work by Nobel prize-winning psychologists Daniel Kahneman and Amos Tversky).

The example starts with ‘Steve’, a shy retiring individual. We are asked to say which is more likely – that he is a librarian, or a farmer? Many people will say ‘librarian’, but that is to ignore how many librarians and how many farmers there are in the general population. The example suggests there are 20 times as many farmers as librarians, so we start with 10 librarians and 200 farmers and no one else.  Consequently,

Prob(Librarian) is 10/(10 +200) = 1/21.

Prob(Farmer) is 200/(10 +200) = 20/21

The video has 4 of the 10 Librarians being shy and 20 of the 200 farmers being shy; a calculation shows how the evidence revises our thinking:

Prob(Librarian| shyness) = 4/(4+20) = 4/24 = 1/6

Prob(Farmer| shyness) = 20/(4+20) = 5/6

‘Steve’ is NOT more likely to be a librarian. The probability that ‘Steve’ is a librarian is actually one in six. Bayesian revision has been calculated and note that the results are normalised.  That is, the 5 to 1 ratio, 20/4, trivially leads to 5/6 and 1/6.  Normalisation is important in order to calculate mean, variance, etc. In more complicated scenarios in many dimensions normalisation remains vital but difficult to obtain.

The problem of normalisation can be solved numerically but not yet in the Metropolis way.  Picture a 24-sided die.  At each roll of the die, record whether the side that comes up is a number 1,2,3 or 4 and call it Librarian. If any other number, 5 to 24, comes up, call it Farmer.  Do this (very) many thousands of times and roughly 1/6 of those tosses will be librarian and 5/6 will be farmer.  This sampling procedure is deemed independent in that a given current toss of the die does not depend on what tosses took place before.  Unfortunately, this straight-forward independent sampling procedure does not work well on more involved problems in higher dimensions.

Metropolis does a specific dependent sampling procedure, in which the choice of where to go next does depend on where you are now but not how you got there, ie  the previous places you visited play no role.  Such a situation is called a Markov process, a concept which  dates from the early 20th century. If we know how to transition from one state to another, we typically seek the long-run probability of being in that state. In the Librarian/Farmer problem, there are only two states, Librarian and Farmer. The Metropolis algorithm says begin in one of the states, Librarian or Farmer, toss a two-sided die which proposes a move.  Accept this move as long as  you do not go down. So, moving from Librarian to Librarian, Farmer to Farmer or Librarian to Farmer are accepted. Moving from Farmer to Librarian may be accepted or not; the choice depends on the relative heights – the bigger the drop, the less likely the move is to be accepted.  Metropolis says: take the ratio, 4/20, and compare to a random number between zero and one. If the random number is less than 4/20, move from Farmer to Librarian; if not, stay at Farmer. Repeat the procedure (very) many, many times.

Typically, there is a burn-in period so the first bunch are ignored and we count from then on the fraction of the runs that we are in the Librarian state or in the Farmer state, to yield the 1/6 and 5/6.

Multiple thousands of iterations today take no time at all; back in World War II, computing was in its infancy and one wonders how many weeks it took to get a run which today, would be done in seconds.  But, so to speak, a door was being constructed. 

In 1970, Hastings introduced an additional term so that for complex cases, the proposals and acceptances would better capture more complex, involved “terrain” than this simple example. In keeping with the doorknob and door imagery, Metropolis Hastings is a better door, allowing us to visit more complicated, elaborate terrain more assuredly and more quickly.  An even newer door, inspired by problems in physics, is known as the  Hamiltonian MCMC.  It is even more complicated, but it is still a door,related to previous MCMC doors.  There are many web sites and videos attempting to explain the details of these algorithms but it is not easy going to follow the logic of every step.  Suffice to say, however, the impact  is enormous and justifies the resurgence of Bayesianism.

Paul Alper is an emeritus professor at the University of St. Thomas, having retired in 1998. For several decades, he regularly contributed Notes from North America to Higher Education Review. He is almost the exact age of Woody Allen and the Dalai Lama and thus, was fortunate to be too young for some wars and too old for other ones. In the 1990s, he was awarded a Nike sneaker endorsement which resulted in his paper, Imposing Views, Imposing Shoes: A Statistician as a Sole Model; it can be found at The American Statistician, August 1995, Vol 49, No. 3, pages 317 to 319.