I am very excited to be part of a resilience and systems thinking programme intended to give participants experience and tools of systems and complexity thinking to address the very complex multidimensional socio-ecological problems we grapple with in our work. This blog post is just a reflection. In the first session, we were given two presentations, which I experienced very differently. Being a scientists, I am very sensitive to accuracy and scientific integrity and that got challenged in the first session.
I was not impressed by the first presentation. It raised red flags, for example the following. One slide showed two plots (reproduced and properly credited below); namely:
- The increase in global surface temperatures as measured by whatever measurements were available since 1960 followed by projections until 2100 as illustrated by the International Panel on Climate Change (IPCC), although the legend was unreadable because the second pot was stuck over it, and
- A plot of vertebrate animal extinctions since 1500 and credited to Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), 2019.
Immediately, my reasoning was as follows: The extinction data only talks about animals, not plant life. The time scales on both plots are completely different (1960 – 2100 vs. 1500 – 2018) and the data are of fundamentally different nature (measures, estimates and “modelled stylised” projections vs. estimates). The plots are related in the sense that it is human activity that is the main driver of both evolutions but putting them next to each can suggest strong correlation if not causality. Not highlighting the huge difference in scales and methods is poor practice. More generally, temperature is not enough to describe extinctions. Just think of loss and fragmentation of habitat because of infrastructure development. Or the different speeds at which a plant ecosystem migrates vs. animals who able to move but who depend on the plant life.
If we are to think about socio-ecological systems, we need to be able to know what the data means and where its meaning ends. Otherwise it will be misinterpreted, biases and preconceptions will take over, and we will mostly have more of the same unsustainable approaches. Scientific integrity and integrity in general is something I value and really don’t like it being neglected. If we don’t have integrity, we can’t learn, we hold on to what we think we know. So things like this make me dig deeper. If I had an uncomfortable feeling about this, I ought to check. Maybe I was wrong and there is something to learn in there?
I quickly found the origins of the two plots. Both original sources of the plots on that slide describe the data and the limitations thereof, but none of it was explained in the slide we were shown, nor in the presentation. It appears to have been put there for its shocking potential. A quick search revealed the second figure in an “unedited draft chapters” IPBES report (PDF, page 39) where credit for it was given and led to a paper by Ceballos et al. 2015(*), not the IPBES. Moreover, the figure is published under a CC BY-NC 4.0 license that requires proper citation. Not giving proper credit is also not a good practice.
Another slide raised red flags. It was a wealth plot and was entitled “Concentration of economic and political power”. The meaning of the plot was unclear, so I had to dig further to understand. I made a note of the reference, which was the web url of the International Social Science Council (now merged with the International Council of Scientific Unions to form the International Science Council with which I am very familiar). It also carried a hashtag, which led me to a 2016 report whose title was the same as the hashtag. In there, I found the plot. It turns out that it is not from that report, but from a 2016 Oxfam policy paper, which I found in the references. Now the plot we were shown had curvy lines – which totally misleads the viewer into thinking that there is continuous data. Curvy lines are the result of mathematical interpolations (e.g. splines) that may be pleasing to the eye but are not data. The version of the same plot in the Oxfam document was much better in that it was a succession of straight lines between data points. At least it shows the data. The data itself, came from two sources. Firstly, a 2015 Credit Suisse “Global wealth” report, where they refer to the 2015 Credit Suisse Global Wealth Databook, where they describe their data and its shortcomings very well. Secondly, the Forbes Billionaires list, where they acknowledge that:
The Forbes World’s Billionaires list is a snapshot of wealth using stock prices and exchange rates from March 18, 2020. Some people become richer or poorer within days of publication. We list individuals rather than multigenerational families who share fortunes, though we include wealth belonging to a billionaire’s spouse and children if that person is the founder of the fortune. In some cases we list siblings or couples together if the ownership breakdown among them isn’t clear, but here an estimated net worth of $1 billion per person is needed to make the cut. We value a variety of assets, including private companies, real estate, art and more. We don’t pretend to know each billionaire’s private balance sheet (though some provide it).
Now that I have a better understanding of the source plot and of the limitations of the data behind the plot, I can assess it. Again, it appeared to have been included for its shocking potential. There are lots of shortcomings to comparing estimated stock holdings and assets such as real estate of a few (why 62?), to the estimated wealth of half the world population, of whom an estimated 1.7 billion (out of 3.5 billion) are unbanked (World Bank Global Index Database 2017), i.e. their wealth is not found in any balance sheet anywhere.
Most of us know that the world is very unequal but introducing it using a plot about monetary wealth seems naive, in a context where we should be looking at complex systems and beyond the obvious. And this plot doesn’t provide evidence of a causal relationship with political power, despite that being the title of the slide. My next red flag about that second slide was the underlying assumption of an ideal model of distribution of political power. It hinted that political power is something to be sought after. But if we are to think differently about the world, thinking of different forms of power is, indeed, powerful. Think of Mahatma Ghandi, whose power was given to him, not bought or ascribed because of wealth. Finally, what is done with monetary wealth matters hugely. If the richest few were all doing things with their currency that was good for the planet, say, then this plot would mean something completely different. Rich people’s ideologies matter. Think how different the global impact of Bill Gates is, from that of Donald Trump.
To conclude, I think we need to be very careful when claiming “let’s see what the data says”. The data we were shown were inaccurately cited, misrepresented and the narrative was misleading. It was full of assumptions about what we, the audience, think and how we would react to the slides we were shown and the narrative we were told. This made me honestly zone out for what came after in that presentation.
(*) When looking at the Ceballos et. al. article published in Science Advances (impact factor 13.116(2019) and published by the AAAS, home of the top journal Science) , I found that the first reference also cited another paper by Ceballos published in 2010 by “the Journal of Cosmology .com”. Being a cosmologist myself, I wondered how a paper about natural life could be published in a journal about cosmology, usually considered a subfield of astronomy, and a journal that I’d never heard of. So of course I looked it up. It turns out that this “journal” has been labeled a predatory publication and appears to promote fringe perspectives on a website promoting pseudoscience books. Looking at some of the latest publications is terrifying. That this reference would make it into a AAAS publication is surprising to say the least and doesn’t contribute positively to the author’s credibility.
The second presentation, although given after a good couple of hours on zoom, was infinitely better. It illustrated powerfully how deeply colonisation determines what we do and how we do it. It was powerful in that it used experience. We were made to think rationally, and feel. And this was a great experience because linking feelings to rational thoughts is a great way of developing the skills needed to spot unconscious bias, even in oneself. When the feeling gets triggered, the rational thought springs to memory and we are enabled with critical thinking.
The second presentation had me taking prolific notes and enjoying doing so. I felt like I was learning something. In really enjoyed it. It was also much more interactive as we were put into small groups to discuss a few questions. I enjoyed hearing what my colleagues in the programme had to say.
We spoke about how local communities are often excluded from natural resource management, that it is often a very centralised top-down approach inherited from colonial times and structures. Indigenous knowledge is overlooked, communities’ participation is marginal at best, and natural resource management from centuries of living sustainably with natural resources until colonisers arrived with an exploitative mindset, is unheard. It was highlighted that local communities need to be returned to ownership of their resources, and freedom to practice their indigenous knowledge. We also discussed how the Global North, with its market forces, shapes for example what crops are grown – often in spite of environmentally-friendly practices. Think of that, next time you buy organic goods. Large-scale monocultures are a colonial legacy. We also heard how the legal frameworks that have to do with the environment are either very new and incomplete, allowing many loopholes, or old and colonial (i.e. exploitative in nature).
Another conversation we had was about humanitarian aid. How do we make humanitarian assistance more equitable? How do we give a voice to those needing the assistance so that they can receive what they need, not what the donors think they need? Our colleague also mentioned that humanitarian assistance is forced upon countries who might actually have the domestic capacity to absorb and manage a humanitarian crisis. It was lamented that humanitarian assistance is often used as a smokescreen for other, political agendas. Think for example the threat of withholding assistance unless a local government adopts a certain policy.
In terms of science I discussed how the 4th industrial revolution is just a name given by a head of a corporate membership organisation that happens to have excellent connections with heads of state. I expressed regret that the 4IR terminology, the “brand”, had led to the setting up of expensive “talk shops” and government policies in small economies with constrained government budgets, instead of seeing the bigger picture, which is that technologies are evolving fast, and are becoming easier to use and to combine, opening the door to lots of innovations, regardless of the name one gives it. With such focus on brand building and terminology, the bigger context is lost. I must also mention that the government’s focus on the term 4IR incentivises people like myself to “surf the wave” and join in the noise, in the hope of getting something good done. This is of course not ideal.
I also mentioned, for science, that Africa is full of invaluable scientific resources. Due to its lesser development in comparison to other places around the world, Africa still has access to clear skies and can therefore do astronomy – in both hemispheres! (Notwithstanding the satellite constellations that trouble astronomers everywhere). I also mentioned that human genetic diversity is greater in Africa than elsewhere in the world, not to mention biodiversity and the potential it presents for pharmaceutical applications. Such scientific resources are sadly still easily extracted by richer scientific communities (the link above is a case in point), published behind paywalls, and ethically moderated by the funding institutions instead of the affected communities. This, to the detriment of African scientists and researchers. Just imagine what scientific wealth Africa could generate for itself were it not for those practices? Finally, I spoke briefly about science advice to government. Often evidence-informed decision making is only possible in projects funded by foreign sources. And while EIDM is a good practice, the particular evidence sought is itself the fruit of the thinking of the funders.
Among all the groups that discussed and then came together again, there was a common conclusion: Donors/funders set the agenda, and donors/funders are the colonial powers and therefore colonial legacy can be found everywhere.
Our speaker also made a very valuable distinction – the fact that something is inherited from colonial thinking and practices doesn’t necessarily mean it needs to be thrown away. It is important to be aware of colonial heritage and influences, but our business must be the future. The past informs it, and looking at it we can see complexity in action. It is not about rebelling, but about going somewhere of our choosing, based on our best thinking.