In my last post, I offered a definition of a “good” election: A “good” election is an election without ambiguity. Some concepts are so broad or complex that they are best defined by what they are not. This is the case, I believe, with elections but further explanation is appropriate. In saying that a “good” election is an unambiguous election is not to say that a “good” election is a perfect election.
There are distinctions we easily make in everyday life such as cleanliness does not require sterility or accuracy does not demand precision. For most things in life, cleanliness and accuracy are all that are needed and expected. Brain surgeons and rocket scientists do, however, expect sterility and precision and they need to know when there is an exception to those states.
Elections are not really brain surgery or rocket science but we tend to think of them in that way in as much as we unreasonably expect the higher standard of sterility and precision over cleanliness and accuracy. No election administrator wants to set an expectation of anything less than perfection. To do otherwise appears lazy, sloppy and irresponsible not to mention politically dangerous. The standard of sterility and precision are sirens songs which lead to false priorities and the acceptance of ambiguity.
I know many hold the view that errors should never be acceptable when it comes to elections. That is an unreasonable standard to which that science doesn’t even hold itself. While recently reading a scientific text on social science research, I came upon a quote that I think is relevant to this topic. The authors, in a discussion of the nature of science, disclose that “…theories do not have to make precisely accurate predictions to be judged as scientifically useful….There is always some degree of error.” (Royce A. Singleton 2010)
Every election- even “good” elections have issues, errors or oversights. I like to call l these things euphemistically “learning moments.” The difference between a bad election and a “good” election is whether or not the learning moments introduce ambiguity in the outcome of the election. Florida in 2000 is the obvious case of ambiguous election results created by chad and the butterfly ballot but ambiguity is not always so obvious.
Here is a list of common situations that often lead to ambiguity in elections:
• Ballot errors and poor ballot design,
• Delays in mailing ballots,
• Last minute changes to polling locations,
• Poorly trained poll workers, inadequate staffing,
• Absence of adequate and documented procedures for key election activities,
• Disregard of documented procedures,
• Inconsistent application of signature verification standards, provisional ballot adjudication and voter intent interpretations,
• Poor testing protocols and testing documentation,
• Inadequate security and chain of custody,
• Weak or absent capability to audit or reconstruct election events,
• Inability to statistically describe and document the election,
• Inability to replicate election processes from one election to the next,
• Inability to consistently estimate or calculate the actual cost and expense of an election,
• Responses to unforeseen election day events.
The presence, itself, of any one or more of these examples does not prevent an election from being judged as “good.” An election cannot be considered “good” when the effect of these situations introduces doubt and ambiguity and raises questions that cannot be adequately addressed.
An election that avoids learning moments and ambiguity by sheer luck and good fortune (rather than by effective procedures and management) is not, by definition, a “good” election.
A discussion of leveraging and overcoming “learning moments” is always in order. I’d like to hear your thoughts.
Royce A. Singleton, Jr. and Bruce C. Straits. Approaches to Social Research. 5th. New York: Oxford University Press, 2010.