Monthly Archives: October 2017

Performance Based Research Fund: a net zero sum game

Throughout the land more than 7000 academics are awake night after night and suffering.  They are scrambling to gather evidence of just how great they have performed over the last six years. A conscientious bunch, they perform this task with their usual attention to detail and desire to impress (I didn’t say they were modest!).  Ostensibly, this exercise is so that their institutions can get a greater piece of the Government research fund pie – the Performance Based Research Fund (PBRF).  According to the Tertiary Education Commission PBRF is “a performance-based funding system to encourage excellent research in New Zealand’s degree-granting organisations.”  It may well do that, but, I contend, only by deception.

In what follows I am only concerned with the Quality Evaluation part of PBRF – that’s the bit that is related to the quality of the Evidence Portfolio (EP) provided by each academic. The data is all taken from the reports published after each funding round (available on the TEC website).

In 2012 the total funding allocated on the basis of EPs was $157 million with nearly 97% of it allocated to the country’s 8 universities.  This total amount is set by Government fiat and, here is the important point, in no way depends on the quality of the Evidence Portfolios provided by those 7000+ academic staff.   In other words, from a funding perspective, the PBRF Quality Evaluation round is a net zero sum game.

PBRF Quality Evaluation is really a competition between degree granting institutions.  I find this strange given the Government has been trying to encourage collaboration between institutions through funding of National Science Challenges, nevertheless a competition it is.

In the table we see the results of the Quality Evaluation for the previous three funding rounds ( 2003, 2006 and 2012).  Not surprisingly, the larger universities get a larger slice of the pie.  The pie is divvied up according to a formula that is based on a weighting for each academic according to how their research has been evaluated (basically A, B or C), multiplied by a weighting according to their research area (eg law and arts are weighted lower than most sciences, and engineering and medicine are weighted the highest), multiplied by the full time equivalent status of the academic.   In theory, therefore, an institution may influence their proportion of funding by (1) employing more academics – but this costs more money of course, so may be defeating, (2) increasing the proportions of academics in the higher weighted disciplines (some may argue this is happening), and (3) increase the numbers of staff with the higher grades.  I will leave it to others to comment on (1) or (2) if there is evidence for them.  However (3) is the apparent focus of all the activity I hear about at my institution.   There are multiple emails and calls to attend seminars, update publication lists, and to begin preparing an Evidence Portfolio.  Indeed, in my university we had a “dry run” a couple of years ago, and it is all happening again.

Now, I come to the bit where I probably need an economist (it is my hope that this post may influence one to take up this matter more).  Because it is a net-zero sum game, what matters is a cost-benefit analysis for individual institutions.  That is, what does it cost the institutions to gather EPs compared to what financial gain is there from the PBRF Quality Evaluation fund?  If we look at the 20012-2006 column we see the change in percentage for each institution.  The University of Auckland for example increased its share of the pie by 1.3% of the pie.  This equates to a little under $2M a year.  As the evaluations happen only every 6 years we may say that Auckland gained nearly $12M.  What was the cost? How many staff for how long were involved?   As there are nearly 2000 staff submitting EPs from Auckland another way of looking at this is that the net effect of the 2012 Quality Evaluation round was a gain of less than $6000 per academic staff member over 6 years.  How much less is unknown.

The University of Otago had a loss in 2012 compared with 2006.  Was this because it performed worse – not at all, indeed Otago increased how many staff and the proportion of staff that were in the “A” category and in the “B” category. This suggests improved, not worsened, performance.  I think that Otago’s loss was simply due to the net zero sum game.

Much more could be said and questions asked about the Quality Evaluation, such as what is the cost of the over 300 assessors of the more than 7000 EPs?  Or perhaps I could go on about the terrible use of metrics we are being encouraged to use as evidence of the importance of the papers we’ve published.  But, I will spare you that rant, and leave my fellow academics with the thought – you have been deceived, PBRF Evidence portfolios are an inefficient and costly exercise which will make little to no difference to your institution. 

Advertisements

Flourish with change

Newshub decided to do an “AI” piece today. Expect much more of this kind of “filler” piece. They will go thus… “X says AI will take all our jobs, Y says AI will save us.” These pieces are about as well informed and informing as a lump of 4×2 – good for propping up a slow news day, but not much else. The “more compassionate and moral than NZers” message (which comes from Y) type statement that was made is utter nonsense. AI is just a name we give to the software of machines – AI don’t have compassion or morals. If they appear too, that is simply because they are reflecting the data we feed them… human data with all its flaws.
 
Yes, there is change coming because of this technology. In the past we have been particularly poor at predicting what the future will look like & I think this time the possibilities are far too numerous and complex for us to predict what will be.  Statements like “30-50% of people will lose their jobs” (said X) are simply guesses because there is no precedent on which to base the numbers. All the reports talk about truck drivers and accountants loosing jobs and not a lot else. They are shallow – and probably necessarily so – because we just can’t anticipate what creative people may come up with for this technology.  Having said that, I must admit I just am not sure what to advise my children (as if they’d take it).  Should they all learn to code? Maybe not, as most interaction with machines may not be via coding languages. Should they become artisans for niche markets where the technology doesn’t penetrate?  Maybe for some, but not for all.  I think that perhaps the best we can do is to encourage what enhances creativity and resilience to, or even better a flourishing with, change. It is my hope that flourish with change will become the mantra not just the next generation, but for all current generations, for how we determine to approach the coming changes is likely as important to the well being of our society as the changes themselves.

This is what happens when you talk to your mother about artificial intelligence

Artificial Intelligence 

Artificial Intelligence

So we don’t need to think.

Everything is done for us

In just an eyelid blink.

 

Artificial Intelligence

So we don’t need to think.

Just take the Robot, plug it in

And go and have a drink.

 

When you come back your work is done;

You haven’t even thunk.

The Robot’s done the washing too;

Oh dear, I think it’s shrunk.

 

Perhaps I shouldn’t have bought this one,

I didn’t even think,

I got it second hand you see

From prisoners in the clink.

 

And when they programmed it you see

I think that they were drunk

‘Cos now it’s full of nasty words;

I really should have thunk.

 

So artificial Intelligence

Depends upon the thought

That someone programmes into it,

And that may come to nought.

 

And so beware when buying one,

You may be feeling sunk,

It may be right for it to think,

But you also should have thunk!

(c) K.A. Pickering, October 2017

AI Robot copy (1)

Artificial Intelligence (c) K.A. Pickering, October 2017