↓ Skip to main content

Designing cultural multilevel selection research for sustainability science

Overview of attention for article published in Sustainability Science, November 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
twitter
15 X users

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
59 Mendeley
Title
Designing cultural multilevel selection research for sustainability science
Published in
Sustainability Science, November 2017
DOI 10.1007/s11625-017-0509-2
Pubmed ID
Authors

Michelle A. Kline, Timothy M. Waring, Jonathan Salerno

Abstract

Humans stand out among animals in that we cooperate in large groups to exploit natural resources, and accumulate resource exploitation techniques across generations via cultural learning. This uniquely human form of adaptability is in large part to blame for the global sustainability crisis. This paper builds on cultural evolutionary theory to conceptualize and study environmental resource use and overexploitation. Human social learning and cooperation, particularly regarding social dilemmas, result in both sustainability crises and solutions. Examples include the collapse of global fisheries, and multilateral agreements to halt ozone depletion. We propose an explicitly evolutionary approach to study how crises and solutions may emerge, persist, or disappear. We first present a brief primer on cultural evolution to define group-level cultural adaptations for resource use. This includes criteria for identifying where group-level cultural adaptations may exist, and if a cultural evolutionary approach can be implemented in studying a given system. We then outline a step-by-step process for designing a study of group-level cultural adaptation, including the major methodological considerations that researchers should address in study design, such as tradeoffs between validity and control, issues of time scale, and the value of both qualitative and quantitative data and analysis. We discuss how to evaluate multiple types of evidence synthetically, including historical accounts, new and existing data sets, case studies, and simulations. The electronic supplement provides a tutorial and simple computer code in the R environment to lead users from theory to data to an illustration of an empirical test for group-level adaptations in sustainability research.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 20%
Student > Ph. D. Student 11 19%
Student > Master 10 17%
Student > Doctoral Student 7 12%
Other 5 8%
Other 7 12%
Unknown 7 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 20%
Environmental Science 11 19%
Social Sciences 7 12%
Psychology 4 7%
Economics, Econometrics and Finance 3 5%
Other 10 17%
Unknown 12 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 September 2019.
All research outputs
#2,072,415
of 24,980,180 outputs
Outputs from Sustainability Science
#194
of 897 outputs
Outputs of similar age
#45,225
of 449,732 outputs
Outputs of similar age from Sustainability Science
#4
of 22 outputs
Altmetric has tracked 24,980,180 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 897 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.5. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 449,732 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.