Analysing extinct storylines

Why bother?

Up to now, most of the discussion on this website about the analysis of storylines has been focused on surviving storylines i.e. those that were continued up until and including the final iteration. However there is a good argument for paying equal attention, if not more so, to the extinct storylines i.e. those that were discontinued at some point before the final iteration. The main reason for doing so is that the number of possibly extinct storylines grows with each new additional iteration. Whereas the number of surviving storylines will always equal the number of participants, it will not change unless some participants drop out during the exercise. So for example, in a recent exercise on the world after COP26 there were 10 surviving storylines, but 32 extinct storylines. During the exercise there were 98 individual contributions of texts which made up of storylines, and 55 of these ended up within extinct storylines.

A typology of extinct storylines

There are different types of extinct storylines. Some extinct storylines had their beginnings in the first iteration. Twenty of the thirty-two extinct storylines were of this kind. The remaining twelve were extinct branches of storylines that did survive until end of the exercise. Within each of these two types of extinct storylines there is one contribution/paragraph that is probably the most important of all. This is the final paragraph in the extinct storyline, after which none of the participants decided to add any extending content.

Analysis options

  • Examine who has contributed to a storyline and whether it subsequently becomes extinct. This analysis might best focus on contributions to the final paragraph in the extinct storylines. It is possible that some people’s contributions are being conspicuously ignored, for one reason or another.
  • Use the keyword search facility to identify if there are conspicuous differences in the location of keywords of interest in surviving versus extinct storylines. All search results will automatically provide such numbers.
  • Use a word-counter app to compare the frequencies of words found in surviving versus extinct storylines
  • In the course of any existing content analysis using manually coded themes compare their incidence in surviving versus extinct storylines.
  • Use supervised machine learning, as described in this example here. I have not yet tried this…

A wider perspective

This quote prompted some thoughts on the subject:

‘If 99% of all species that have lived on earth are extinct, it follows that the total of species originations has been virtually the same as the total of species extinctions. Although present biodiversity – the millions of living species – seems high to us today’s biota results from a minor surplus of speciation is over extinctions, accumulated over a long time. In the view of these figures, it is puzzling that even evolutionary biologists have devoted almost no attention to extinction. Large monographs and textbooks have been written about speciation, and careers have been built around the subject. But extinction has barely been touched. It’s like a demographer trying to study population growth without considering death rates or an accountant interested in credits but not debits”

Evolution extended: biological debates on the meaning of life. Edited by Connie Barlow. 1994

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