Blaming the Media - Ten Years to Prevent Catastrophe

A review of the above titled paper by Hugh Doulton, Katrina Brown.

Climate change is, rightly or wrongly, still a contested issue in all its dimensions—scientific, political, economic and social (Carvalho, 2003). The mass media is a critical arena for this debate, and an important source of climate change information for the public. The science of climate change is full of uncertainty, however, the greater vulnerability of poor countries to the impacts of climate change is one aspect that is widely acknowledged.

The paper adapts Dryzek’s (2005) ‘components’ approach to discourse analysis to explore the media construction of climate change and development in UK ‘quality’ newspapers between 1997 and 2007. Eight discourses are identified from more than 150 articles, based on the entities recognised, assumptions about natural relationships, agents and their motives, rhetorical devices and normative judgements. They show a wide range of opinions regarding the impacts of climate change on development and the appropriate action to be taken.

The term ‘discourse’ has many definitions; here it is understood as ‘a shared meaning of a phenomenon’ (Adger et al., 2001).

Discourses concerned with likely severe impacts have dominated coverage in the Guardian and the Independent since 1997, and in all four papers since 2006. Previously discourses proposing that climate change was a low development priority had formed the coverage in the Times and the Telegraph.

The classification of different discourses allows an inductive, nuanced analysis of the factors influencing representation of climate change and development issues; an analysis which highlights the role of key events, individual actors, newspaper ideology and wider social and political factors. Table 1 below gives a summary of the 8 different discourses. (Click on the table for a better look, both images contribute to the same table). The table displays how they are distinguished and how they are constructed through the basic entities recognized; the assumptions about natural relationships; the agents; metaphors and rhetorical devices; and normative judgments. The sources of authority range from climate/skeptical science, to NGOs and individuals.

Table 1.

Authors have shown that the media frequently fails to convey scientific uncertainty regarding climate change accurately, tending to sensationalism and increased certainty, despite major inherent uncertainty in climate predictions (Ladle et al., 2005).

In all the discourses other than optimism and self-righteous mitigation (table 2.), developing countries are portrayed as needing the help of the developed world if they are to deal with the impacts of climate change. There is little discussion of poor people in dealing with the impacts of climate change, nor the complex interplay of factors that will influence the vulnerability and adaption to climate change (Adger et. al., 2003). Only ‘disaster strikes’ gives any voice to poor people, as well as barely any differentiation of the varied developing world itself.

Thus the overall the findings demonstrate media perceptions of a rising sense of an impending catastrophe for the developing world that is defenseless without the help of the West, perpetuating to an extent views of the poor as victims.

References

Adger, W.N., Huq, S., Brown, K., Conway, D., Hulme, M., (2003). Adaptation to climate change in the developing world. Progress in Development Studies 3 (3), 179.

Carvalho, A. (2003). Reading the papers: ideological cultures and media discourses on scientific knowledge. Paper presented at a conference entitled Does Discourse Matter? Discourse Power and Institutions in the Sustainability Transition, Hamburg, Germany, pp.11-13.

Dryzek, J.S. (2005). The Politics of the Earth: Environmental Discourses. Oxford University Press.

Ladle, R.J., Jepson, P., Whittaker, R.J., 2005. Scientists and the media: the struggle for legitimacy in climate change and conservation science. Interdisciplinary Science Reviews 30 (3), 231–240.

Early warning signals and the prosecutor’s fallacy

So continuing on from the last blog, might our warning systems on tipping points have been hindered?

Early warning systems have been proposed to forecast the possibility of a critical transition, such as the eutrophication of a lake, the collapse of a coral reef or the end of a glacial period. Because such transitions often unfold on temporal and spatial scales that can be difficult to approach by experimental manipulations, research has often relied on historical observations as a source of natural experiments.

Here, Boettiger and Hastings, examine a critical difference between selecting systems for study based on the fact that we have observed a critical transition and those systems for which we wish to forecast the approach of a transition. This difference arises by conditionally selecting systems known to experience a transition of some sort and failing to account for the bias this introduces - a statistical error known as the prosecutor’s fallacy. The term is however most often associated with prosecuting lawyers arguing for the guilt of a defendant in a criminal trial.

By analyzing simulated systems that have experienced transitions purely by chance, they reveal an elevated rate of false-positives in common warning signal statistics.

The attempts to detect early warning signs for critical transitions are based on the concept of deteriorating environment as embodied in a changing parameter (Scheffer et. al., 2009), which is a different kind of transition than the alternative of stochastic system (i.e. non- deterministic, so probabilities are used to work out potential outcomes) in an environment that is otherwise constant and exhibiting no directional change. When trying to use historical data to understand critical transitions, we often do not know which category, changing environment or simply chance, an observed large change falls into, which leads to uncertainty.

Boettiger and Hastings have shown here that systems that undergo rare sudden transitions owing to chance look statistically different from their counterparts that do not, even though they are driven by the same stochastic process (non-deterministic).

In particular, such conditionally selected examples are more likely to show signs associated with an early warning of an approaching tipping point, such as increasing variance or increasing autocorrelation, as measured by Kendall’s  (used to measure the association between two measured quantities).

This increases the risk of false positives - cases in which a warning signal being tested appears to have successfully detected an underlying change in the system leading to a tipping point, when in fact the example comes instead from a stable system with no underlying change in parameters.

It does seem tempting to argue that this bias towards positive detection in historical examples is not problematic each of these systems did indeed collapse; so the increased probability of exhibiting warning signals could be taken as successful detection. Unfortunately this isn’t the case. At the moment the forecast is made, these systems are not likely to transition, because they experience a strong pull towards the original stable state. As the system gets farther from its stable point, it is more likely to draw a random step that returns it towards the stable point. However of course there is also the chance that it will continue away from its original stable point, thus any systems that do cross a tipping point would do so rather quickly.

The authors do also go on to demonstrate a model-based approach that is less subject to this bias than those more commonly used in summary statistics as well as highlight the fact that experimental studies with replicates avoid this pitfall entirely – largely through running many models and improving knowledge of the system to remove bias.  However I think that’s enough for now and this new method is still to be fully evaluated and/or used by the scientific community.

Read the full paper at:  http://rspb.royalsocietypublishing.org/content/279/1748/4734.full.pdf+html

Reference:

Boettiger, C. and Hastings, A. (2012) Proc. R. Soc. B  vol. 279, no. 1748. 4734–4739.

Scheffer, M. et al. 2009 Early-warning signals for critical transitions. Nature 461, 53–59. (doi:10.1038/nature08227)