An assertion can’t be proven by a lack of evidence to the contrary, but that’s what I’m going to do anyway. Have you ever read a study or seen a report where the result of the analysis was that the resilience of the particular community was zero? The sum of all the normalized demographic variables you choose was zero? The “ resilience triangle ” was just a flat line–theta of the triangle equal to zero?
I doubt it. I definitely haven’t. If you have, please let me know what method was used to quantified a community as having no resilience.
I don’t think this is an academic question either. I think its a fundamental one that deconstructs the most common question related to the study and analysis of community resilience: Is that community resilient?
The question leaves open the possibility of the answer, no, it isn’t; the community has a resilience of zero.
All communities are relevant to some degree. You could capture this by answer the question of whether a community is resilient on, for example, a scale of 1 to 10 or 0 to 1.
Certainly, we can create methodologies that can put a single number to that question. But why would we want to? How useful is a single number characterizing a community’s resilience. What decisions are the numbers intended to assist with? Who’s the intended audience. What is the research supporting the choice of the metrics and data?
Worthwhile questions for us to ponder.
All communities have aspects of it that are more or less resilient. Some resilient aspect of a community are not a positive thing, at least to some people.
So rather than some single number that represents resilience, any methodology should facilitate exploration of the hyper-variate and contextual nature of community resilience. I represent this idea with the image for this post, showing a GUI mockup–“sparklines” of recovery across space, time, and various aspects of resilience.
Methodological questions about community resilience should be contextual, relative, and open-ended. How is the particular aspect of a community resilient, with respect to whom and in relation to what? This type of analysis requires the development of simulation models that include elements of time, space, and geographic scale. And imaginative methodological questions have to be based on theory .
The common questions framing most methodologies for assessing community resilience oversimplify the theory of resilience . More likely, the methodologies aren’t based on any theory of community resilience at all, rather being based on conveniently accessible and quantifiable data.
It follows that the next most common question is not methodologically (very) useful either: How do we make that community resilient?
You don’t have to do anything to make that community resilient. It’s already resilient.