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1. Interdependent Infrastructure as Linked Social, Ecological, and Technological Systems (SETSs) to Address Lock‐in and Enhance Resilience

   https://doi.org/10.1029/2018EF000926  

   Markolf, Samuel A.; Chester, Mikhail V.; Eisenberg, Daniel A.; Iwaniec, David M.; Davidson, Cliff I.; Zimmerman, Rae; Miller, Thaddeus R.; Ruddell, Benjamin L.; Chang, Heejun (December 2018, Earth's Future)

   Abstract Traditional infrastructure adaptation to extreme weather events (and now climate change) has typically been techno‐centric and heavily grounded in robustness—the capacity to prevent or minimize disruptions via a risk‐based approach that emphasizes control, armoring, and strengthening (e.g., raising the height of levees). However, climate and nonclimate challenges facing infrastructure are not purely technological. Ecological and social systems also warrant consideration to manage issues of overconfidence, inflexibility, interdependence, and resource utilization—among others. As a result, techno‐centric adaptation strategies can result in unwanted tradeoffs, unintended consequences, and underaddressed vulnerabilities. Techno‐centric strategies thatlock‐intoday's infrastructure systems to vulnerable future design, management, and regulatory practices may be particularly problematic by exacerbating these ecological and social issues rather than ameliorating them. Given these challenges, we develop a conceptual model and infrastructure adaptation case studies to argue the following: (1) infrastructure systems are not simply technological and should be understood as complex and interconnected social, ecological, and technological systems (SETSs); (2) infrastructure challenges, like lock‐in, stem from SETS interactions that are often overlooked and underappreciated; (3) framing infrastructure with aSETS lenscan help identify and prevent maladaptive issues like lock‐in; and (4) a SETS lens can also highlight effective infrastructure adaptation strategies that may not traditionally be considered. Ultimately, we find that treating infrastructure as SETS shows promise for increasing the adaptive capacity of infrastructure systems by highlighting how lock‐in and vulnerabilities evolve and how multidisciplinary strategies can be deployed to address these challenges by broadening the options for adaptation. 

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2. Novel pathogen introduction triggers rapid evolution in animal social movement strategies

   https://doi.org/10.7554/eLife.81805  

   Gupte, Pratik Rajan; Albery, Gregory F; Gismann, Jakob; Sweeny, Amy; Weissing, Franz J (August 2023, eLife)

   Animal sociality emerges from individual decisions on how to balance the costs and benefits of being sociable. Novel pathogens introduced into wildlife populations should increase the costs of sociality, selecting against gregariousness. Using an individual-based model that captures essential features of pathogen transmission among social hosts, we show how novel pathogen introduction provokes the rapid evolutionary emergence and coexistence of distinct social movement strategies. These strategies differ in how they trade the benefits of social information against the risk of infection. Overall, pathogen-risk-adapted populations move more and have fewer associations with other individuals than their pathogen-risk-naive ancestors, reducing disease spread. Host evolution to be less social can be sufficient to cause a pathogen to be eliminated from a population, which is followed by a rapid recovery in social tendency. Our conceptual model is broadly applicable to a wide range of potential host–pathogen introductions and offers initial predictions for the eco-evolutionary consequences of wildlife pathogen spillover scenarios and a template for the development of theory in the ecology and evolution of animals’ movement decisions. 

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3. Keyword expansion techniques for mining social movement data on social media

   https://doi.org/10.1140/epjds/s13688-022-00343-9  

   Bozarth, Lia; Budak, Ceren (May 2022, EPJ Data Science)

   Abstract Political and social scientists have been relying extensively on keywords such as hashtags to mine social movement data from social media sites, particularly Twitter. Yet, prior work demonstrates that unrepresentative keyword sets can lead to flawed research conclusions. Numerous keyword expansion methods have been proposed to increase the comprehensiveness of keywords, but systematic evaluations of these methods have been lacking. Our paper fills this gap. We evaluate five diverse keyword expansion techniques (or pipelines) on five representative social movements across two distinct activity levels. Our results guide researchers who aim to use social media keyword searches to mine data. For instance, we show that word embedding-based methods significantly outperform other even more complex and newer approaches when movements are in normal activity periods. These methods are also less computationally intensive. More importantly, we also observe that no single pipeline can identify little more than half of all movement-related tweets when these movements are at their peak mobilization period offline. However, coverage can increase significantly when more than one pipeline is used. This is true even when the pipelines are selected at random. 

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4. The associations of social and motivational factors to science and mathematics teacher retention.

   https://doi.org/https://learntechlib.org/pv/220830/  

   Ekmekci, A; Aqazade, M; Papakonstantinou, A; Orcan, B; Catanese, J; Callard, C; Cerosaletti, C; Daley, M; Rushton, G; Reid, J; et al (January 2022, Proceedings of 2022 Society for Information Technology & Teacher Education International Conference)

   Langran, E (Ed.)

   Teacher turnover in science and mathematics is a significant and consistent challenge for K-12 education in the U.S. This paper provides: (a) an investigation of the relationship between teacher retention and several social and motivational factors; and (b) a comparison of Master Teaching Fellows (MTF) and non-MTF teachers in terms of their retention and social and motivational factors. Teachers are classified into three retention categories: (a) stayers, (b) shifters, and (c) leavers. Social and motivational factors included teaching self-efficacy, diversity dispositions, leadership skills, principal autonomy support, teacher-school fit (adapted from person-organization fit literature), and social networks related to teaching and education. Study 1 included about 250 science and math teachers from the gulf coast region of Texas. Study 2 included 167 science and math teachers across the country. Teachers completed a survey in the summer and fall of 2021. For study 1, multinomial logistics regression analyses indicate: (a) leavers have significantly higher levels of self-efficacy; and (b) shifters have significantly higher levels of leadership skills and lower levels of teacher-school fit. The second study findings indicate: (a) MTFs’ teacher leadership network and teaching self-efficacy are significantly greater than that of non-MTFs’; and (b) MTFs significantly tend to shift to a leadership position than non-MTFs do. 

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5. The associations of social and motivational factors to science and mathematics teacher retention

   Ekmekci, A. and (April 2022, Proceedings of 2022 Society for Information Technology & Teacher Education International Conference)

   Teacher turnover in science and mathematics is a significant and consistent challenge for K-12 education in the U.S. This paper provides: (a) an investigation of the relationship between teacher retention and several social and motivational factors; and (b) a comparison of Master Teaching Fellows (MTF) and non-MTF teachers in terms of their retention and social and motivational factors. Teachers are classified into three retention categories: (a) stayers, (b) shifters, and (c) leavers. Social and motivational factors included teaching self-efficacy, diversity dispositions, leadership skills, principal autonomy support, teacher-school fit (adapted from person-organization fit literature), and social networks related to teaching and education. Study 1 included about 250 science and math teachers from the gulf coast region of Texas. Study 2 included 167 science and math teachers across the country. Teachers completed a survey in the summer and fall of 2021. For study 1, multinomial logistics regression analyses indicate: (a) leavers have significantly higher levels of self-efficacy; and (b) shifters have significantly higher levels of leadership skills and lower levels of teacher-school fit. The second study findings indicate: (a) MTFs’ teacher leadership network and teaching self-efficacy are significantly greater than that of non-MTFs’; and (b) MTFs significantly tend to shift to a leadership position than non-MTFs do. 

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