The seesaw metaphor of leverage points for sustainability

By Joern Fischer

A picture is worth a thousand words — and an effective graphic can summarise complex relationships in simple terms. Perhaps the most famous graphical metaphor in sustainability science is the ball-in-cup metaphor used to communicate the concept of ecological resilience. What makes the ball-in-cup metaphor so powerful? And can the seesaw metaphor of leverage points (illustrated below) be equally powerful?

The ball-in-cup metaphor describing resilience — especially the bottom part of this figure has been very influential (Source: Kuei-Hsien Liao 2012, Ecology & Society)

Before we get to the seesaw metaphor of leverage points for sustainability, let’s “analyse” some of the key features of the ball-in-cup metaphor.

1. The ball-in-cup metaphor addresses an important issue that had been under-recognised. The concept of ecological resilience, when it was first raised, was new, and not entirely easy to understand to scientists or the public. People were used to thinking of the world as one that ought to be stable, and which, when “shocked”, would return to this stable state. The idea that a system might in fact “flip” into an alternative stable state was fairly different from what most people were thinking about. Today, thinking about these issues has culminated in the concept of “planetary boundaries”, that is, the notion that we might move our Earth system into an entirely different, and less livable, stable state. Most scientists and many members of the public can understand the idea of “planetary boundaries” — of moving outside the desired stability domain. Arguably, this would not have been the case without the “ground work” being done for a number of years by the simple ball-in-cup metaphor.

2. The ball-in-cup metaphor captures just enough complexity to be interesting, but no more. The concept of ecological resilience has many subtleties, which have in fact been captured graphically in various other ways. For example, some systems are characterised by hysteresis, that is, changes between alternative stable states that are not smoothly reversible. Also, systems may change their state not because of a shock, but because of a change in contextual conditions. Subtleties such as these can be drawn using the ball-in-cup metaphor, but not without some difficulties. It’s the basic ideas, however, that are most important to understanding ecological resilience, and that are captured really well by the ball-in-cup metaphor: (i) your system sits in one spot, but don’t take that as given; (ii) if you shock it a bit, it might recover and stay basically the same; but … (iii) if you shock it too much, it may flip into another state. This is relatively complex material to get your head around, but using the ball-in-cup metaphor, it’s fairly easy to explain.

3. The ball-in-cup metaphor makes intuitive sense. We get it: most of us have seen a ball roll down a hill, or be kicked over the top of one … and that’s about all you need, in terms of personal experience, to get this metaphor.

4. It’s imperfect, but not to the degree that it leads to wrong conclusions. The world is a lot more complex than a ball in a cup. Many of the mechanisms keeping the ball where it is — or driving it over the edge — remain hidden in this metaphor. Stability domains typically involve feedbacks to keep the system where it is. It’s the nature of these feedbacks (or new ones not previously operating) that drives system change, and causes regime shifts. One could criticise that these kinds of changes remain invisible in the ball-in-cup metaphor. It also doesn’t say anything about the nature of the drivers, and one might argue that thinking about the world as a simple ball ignores the fact that it’s the world itself that is dynamic — isolating the ball from its cup thus might be considered intellectually meaningless — when it comes to the Earth system, the ball and the cup are essentially the same thing, and even the people causing the changes (the drivers) are part of it. While these are potential criticisms, I’d just shrug them off in the sense that no metaphor can be perfect. But if it’s good enough to get an important point across, it can be powerful nevertheless.

So, what about the seesaw metaphor of leverage points for sustainability? The idea of leverage points dates back to a paper by Donella Meadows (as previously explained here). To communicate our research project on leverage points, we have simplified this using the graphical metaphor of a seesaw (in fact, the original credit goes to Dave Abson).

Meadows’ leverage points, depicted here using the metaphor of a seesaw.

How does this metaphor stack up against the features that seem to have made the ball-in-cup so useful?

Regarding point 1 — the importance of the issue at hand — I think we’re spot-on. Despite ever-increasing efforts, there is little sign that humanity (globally speaking) is moving towards sustainability, and there are plenty of signs that we’re moving away from it. A lot of what we’re doing is fiddling around the edges, while leaving some of the key issues unaddressed. In 1971, Ehrlich and Holdren spoke of environmental impact being a function of population, affluence and the technologies we use. Perhaps population is starting to level off, but hunger for affluence remains unsatisfied even among the richest, and in sum, the technologies humanity uses today are no less “impact-free” than several decades ago (though, of course, some technologies can alleviate the pressures caused by population and affluence). What’s missing from current solutions? Too many of the things we discuss don’t even go near those leverage points discussed by Meadows as the more influential ones. The system rules, the system goal, the paradigm driving the system, and the will to question it — addressing these things is where we could expect major change to come from. Not from adjusting constants or changing a few material flows. This point is both important and under-recognised.

Regarding point 2 — capturing enough complexity but no more — the seesaw metaphor is also quite useful. There are many things that this metaphor does not tell us, but there are also a few key things that it says quite immediately — push at the end of the seesaw, if you want to move it: we’ve been pushing in the wrong spot!

Regarding point 3, the seesaw is fairly intuitive. Perhaps not everybody has actually played on a seesaw … I suspect there are a lot more kids who kick around a ball than who sit on a seesaw! But as far as these things go, many of us have at least seen a seesaw in action, and so the metaphor is somewhat intuitive.

Regarding point 4: like the ball-in-cup metaphor, the seesaw metaphor is not perfect. Again, the complex system is being dumbed down to a “round thing”, and again, human agency sits outside that round thing (and not within it). Perhaps more importantly, the labels from low to high leverage points are driven by the expertise of one (albeit hugely experienced!) systems researcher, namely Donella Meadows. Despite her undeniable wisdom, her list of leverage points may not be perfect, and in her essay, she herself acknowledged that she had changed her mind on the relative importance of these leverage points through time. But that’s where research comes in: science ought to look at those things that are potentially important, but largely under-researched.

There’s no telling if the seesaw metaphor will speak to the world — and it would be crazy to even hope that it can be as successful as the ball-in-cup metaphor. But this isn’t a competition, after all! — It’s about getting important ideas across to as many people as possible.

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How I see qualitative coding

By Andra Ioana Horcea-Milcu

When answering to reviews of qualitative or semi-qualitative interdisciplinary papers, I noticed researchers may be sometimes challenged to explain how they arrived to the qualitative results they present. This made me think several times about how I analyze my own interview data, which often comes down to explaining how I code the data, which is potentially more difficult than the coding itself. I would like to deconstruct the way I perform or rather see coding in this blog entry. More specifically, I would like to emphasize one of the aspects that, to my mind, is often overlooked. As a disclaimer, I would like to mention there is a lot of literature on the different technicalities, types, cycles, etc. of coding, and I believe there is no such thing as a single recipe to these. Here, I aim to provide an intuitive meta-view on coding while I am still learning about it.

Coding: a 3D process

Broadly speaking, qualitative coding is a way of operationalizing the analysis of qualitative data by labeling fragments of it (Bryman 2012). I like to look at coding as a 3D process (Fig. 1) having its origin (0,0) in the research question. Simply put, one dimension are the themes, another one the concepts, and the third dimension is the interpretation.


First dimension

On the OX axis there is the actual content, the expressed themes, the substance. Coding along this axis goes more in the direction of thematic coding and content analysis. It is the more descriptive part of coding and in my opinion, it is also the most grounded stage of it. Themes may stem from prompted and unprompted content, i.e. emergent themes which may remain as a separate category.

Second dimension

I see the OY axis complementary to the first one. Here we find the underlying content, its form, the way it is expressed and constructed, the more abstract notions. This goes more in the direction of discourse analysis, axial coding, abstraction, conceptualization. It is the more analytical stage of coding. Here, I try to look in my data for more abstract concepts, sometimes guided by the literature, based on the refined codes pertaining to the first dimension.

Third dimension

Finally, I use my own knowledge and creativity to link the above together, and meaningfully “lift” the results above the data, in relation to the research question. Although generally more “invisible”, depending on the extent to which coding is aimed at building a theory, the importance of this axis (OZ) increases. It is thanks to this dimension that we are able to interpret data (i.e. attribute meaning to codes, intermediate points on OX and OY). This is also one of the reasons why qualitative data analysis software cannot fully replace the researcher. The importance of this “interference” of the researcher with the result became clear to me when a more senior social scientist recommended me two editorials: Pratt 2009 and Suddaby 2006. The third axis is, in my opinion, the salt and pepper of coding. It is an area of creativity and where the art of coding becomes possible.

The result

Qualitative results don’t just happen. Although qualitative results presented by papers may seem just as sudden as the black dot on Fig. 1, in reality they represent several dimensions. My understanding is that, depending on the research question, the result, what we report as a finding, is somewhere situated along these three axes (or more, as each of them can be further decomposed). Along the axes, we focus, we aggregate, we synthesize, we reduce, ultimately we extract. Processing and analyzing data along each of them is part of the result. For simplification, going along the axes could correspond to different iterative cycles of coding. This (at least) bimodal coding process is quite commonly described by several authors; e.g.: first-order and second-order codes in Pratt 2009, first and second cycle coding in Seldaña 2009, first-level and second-level codes in Tracy 2013. In reality, I think these dimensions are not so clearly separated and may happen simultaneously.

Reviewing coding

When coding, the researcher is inherent to the results. In my opinion this has interesting implications for the review process. It needn’t mean that one should cease looking for objectivity or repeatability or that coding isn’t pragmatic, but rather to acknowledge and recognize the characteristics of qualitative research. Although I am still learning about this, I think that the existence in the research design of elements indicating concern for quality criteria, internal coherence, triangulation, feed-backs, and transparency could increase the credibility of results and make the third dimension more embedded (and justifiable). Saving the coding tree at different moments in time allows for tracking back the way codes evolved through the analysis, if necessary. Keeping a record of iterations or metadata about codes, could be also useful ways to transform coding into a “HD” process.


I tried to briefly discuss the way I think about coding, and convey how I broadly code qualitative data. Probably, every researcher has a different way of approaching coding, and has built and adapted through time his or her own understanding of qualitative coding. There may be many other ways to decompose this process and explain how one arrived to specific results. However, openly acknowledging the third dimension of “this movement of data” would make communication among the more quantitative driven and the more qualitative driven scientists more amicable and enjoyable.


Bryman, A. (2012). Social research methods. Oxford university press.

Pratt, M. G. (2009). From the editors: For the lack of a boilerplate: Tips on writing up (and reviewing) qualitative research. Academy of Management Journal, 52(5), 856-862.

Saldaña, J. (2012). The coding manual for qualitative researchers (No. 14). Sage.

Suddaby, R. (2006). From the editors: What grounded theory is not. Academy of management journal, 49(4), 633-642.

Tracy, S. J. (2012). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact. John Wiley & Sons.

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Start your own research group in Germany

By Joern Fischer

Today, I received a message from the Alexander von Humboldt Foundation, announcing the latest Sofja-Kovalevskaja funding round. This is an outstanding funding instrument — arguably one of the nicest grants one could possibly get. The administrative burden is fairly low and the flexibility of the research programme is high. The award is suitable for people within a few years of their PhDs who have an unusually strong track record (otherwise there is no point in trying). As part of the application, a host institute is needed. If you are interested in pursuing this funding option in an area related to my interests, please let me know. My own funding through this scheme finishes up this year, but given my positive experiences with this programme, I would be happy to consider hosting somebody with a suitable profile and interests.

Here is the message from the Humboldt Foundation:

With the Sofja Kovalevskaja Award, the Alexander von Humboldt Foundation is offering promising young researchers from all over the world attractive career prospects in Germany. Junior research talents of all disciplines from abroad are given the opportunity to establish working groups of their own at German research institutions.

The Sofja Kovalevskaja Award recognises outstanding talent and creative research approaches with exceptional conditions: With an award amount of up to €1.65 million each winner receives valuable starting capital to spend five years pursuing an innovative research project at a research institute of his or her choice – untroubled by administrative constraints. In addition, the establishment of their own junior research team enables the award winners to lay an important foundation for a promising academic career at a very early stage. Eight awards are expected to be granted.

Outstandingly qualified junior academics of all disciplines from abroad who completed their doctorate less than six years ago are eligible to apply for the Sofja Kovalevskaja Award. It is also possible to submit applications immediately after finishing one’s doctoral studies. Applications must be submitted by 31 July 2015

We should be very grateful if you would support our search for young international research personalities by disseminating this announcement at your institution and also asking your colleagues to draw the attention of appropriately qualified research talents to this academic award.

Details of the application procedure for the Sofja Kovalevskaja Award can be found on our website at: For individual questions, you are also welcome to contact

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Calories for a ‘normal life’

Originally posted on monsoontrader:

In the previous post about calorie production from crops across more than thirty countries I quoted the figure of 2,200 calories for a ‘normal life’. This figure is by the United Nations Food and Agriculture Organisation to assess hunger across the globe. Two days after publishing the post a colleague alerted me to a very important critique of the FAOs major report on hunger: the State of Food Insecurity in the World 2012 (SOFI 12). This document entitled Framing Hunger: A response to The State of Food Insecurity in the World 2012 and is a collaboration between 17 people across 7 institutions (Details included in the report).

One major and quite alarming critique is the use of 2,200 calories as the threshold for considering a person hungry. In the post I stated that this number of calories was sufficient for a ‘normal lifestyle’. In fact 2,200 calories is considered sufficient…

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Paper recommendation: Fostering creative thinking in science

I’d like to recommend the following paper: Dual thinking for scientists. Scheffer M, Bascompte J, Bjordam T, Carpenter S … Mazzeo N, Meerhoff M, Sala O, Westley F.  Ecology and Society 2015; 20(2). DOI: 10.5751/ES-07434-200203. Available here.

Figure one, copied from the recommended paper

Figure one, copied from the recommended paper

This paper makes an exceptionally important point. In order to generate useful, genuinely new insights, scientists need to (re-)learn to think intuitively, and balance such intuitive thinking with reasoning. This, in turn, requires space for reflection and informal activities. While this point may be obvious to some, most institutional structures still do not adequately support such non-formalised ways of doing science. This paper nicely reasons, drawing on a range of useful examples and literature, why it is important to balance reasoning with more open-minded, intuitive perception.

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New Masters and PhD positions in Canada (grazing and climate change)

(Introductory note by Joern: Kate Sherren and I have published together on holistic management. It’s an exciting field, and Kate is in the process of setting up a new project on this. The following positions are nice opportunities for motivated individuals looking to work in Canada!)

By Kate Sherren

Resolving the schism in the science and practice of holistic grazing management

Up to 4 Positions – mix of Masters and PhD

Expressions of interest are being sought for Masters and PhD positions. Please register your interest by sending your CV, transcript, and a brief statement of interest to Kate Sherren ( including desired level of study. To all readers of this blog: please help distribute this note — thank you!

Holistic management (HM) is an approach to grazing decision-making based on explicit goal-setting and careful monitoring, often characterized by native pastures and high-intensity but short-duration rotational grazing. Science is bitterly divided on its utility: experimental scientists see no benefits from the constituent practices in controlled experiments, while management-oriented agricultural scientists report benefits at the farm scale, including during drought. To date, producer experience and perceptions have been neglected, but also untested in appropriate ways. This project combines quantitative and qualitative social and information science methods, grounded with insights from agricultural science, to help resolve the schism: drawing a comprehensive picture of a polarized field of study; establishing the value of qualitative methods and producer perceptions in agricultural science; and, exploring HM as a viable climate adaptation strategy for the Canadian Prairies. Aspects of this issue were previously featured on this blog here.

Funded project areas

Several student opportunities are available, with the level flexible (Masters or PhD) depending on the student mix:

  • At Dalhousie, to study global scholarship and policy on grazing and climate using: measures of scientific influence (bibliometrics) to understand the structure of the HM literature; qualitative investigation of its key texts to establish the influence of producer perceptions on science and policy; and/or statement sorting via Q-methodology to identify polarizing concepts as well as common ground.
  • At the University of Alberta, to study Prairie HM trainers, their students, non-HM producers and experts/scholars using quantitative methods and cognitive mapping to understand and compare world views and decision-making.
  • At either university, for a landscape-scale study of livestock producers, using qualitative methods including landscape elicitation, such as farm tours, to explore producer perceptions of their landscape and climate, how these drive management decision-making, and how they align with scientific evidence.

The above topics would suit students with an environmental studies, rural sociology, or agricultural science background, but we are not prescriptive. More important is a strong academic record, an interest in agricultural futures and/or the science/policy interface, and a working style compatible with interdisciplinary team research, as described on this blog here and here. Read more details (including those above), here:

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From information age to century of insight

By Joern Fischer

Data is more abundant than ever. Humanity’s insatiable wants have marked the onset of the Anthropocene, and countless data summarizing the state of the world are available in red lists, food (in-)security indicators and other summary statistics.

Information is also increasingly abundant, and humanity’s various excesses have been summarized aptly in documents like the Millennium Ecosystem Assessment, the series of IPCC reports, or reports on poverty and inequality.

So we have plenty of data and plenty of information. But can humanity leverage the era of big data and the information age to make the 21st century the century of insight?

And is the role of science to use data and generate information, and then hope for the rest of the world to be insightful? Presumably partly yes, but perhaps science doesn’t need to stop at generating information.

Assuming for a moment that just generating more information, on its own, won’t do to make this the century of insight, I wonder if the notion of insightful science might be useful. As opposed to science seeking to generate information, insightful science would seek to generate something that somehow goes deeper, and is more than just proliferation of information. Perhaps all science is more or less insightful, in which case the notion of insightful science may be of little use. But even then, assuming that some scientific endeavours are more insightful than others, this leads to a series of potentially interesting questions:

  1. What are distinctive features of insightful science?
  2. Can these features be fostered in research projects, in research institutions, or by funding agencies?
  3. How can insightful science be made visible, so that it’s not just “big data” that gets the attention, but “big insights”?

For now, I’ll just put those questions out there… if there’s something to the notion of insightful science, it may be worthwhile to explore these points in a future blog post.


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