Uncertainty, risk and ignorance
SSRN, ISSN: 1556-5068
2021
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Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The topic of uncertainty, risk and ignorance, and one may add probability, is controversial both across and within professions. While Frank Knight and Maynard Keynes both believed that risk and uncertainty are quite different objects, generations of Bayesian statisticians and economists have attacked this distinction, arguing that the logic of Bayesian calculus can tame uncertainty. What is the ecologists to do when confronted with risk analyses? What to accept and what to refute? The issue in methodological as well as normative – or political. Some of the fathers of the ecological movement have been outspoken in their warning against excesses of quantification, especially when these serve vested interests under the clothing of risk analysis. In addition, how to deal with ignorance? We offer here some simple heuristics to orient the decision; we shall make use of post normal science, uncertainty analysis, NUSAP, sensitivity analysis and sensitivity auditing.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85121199010&origin=inward; http://dx.doi.org/10.2139/ssrn.3977096; https://www.ssrn.com/abstract=3977096; https://dx.doi.org/10.2139/ssrn.3977096; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3977096; https://ssrn.com/abstract=3977096
Elsevier BV
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