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1. Forest Management and Adaptation Strategies in Response to Climate Change by the Taiwanese Public

   https://doi.org/10.3390/atmos12081056  

   Wan-Yu Liu, Chien-Chen Wu ( August 2021 , Atmosphere)

   Forests account for 60% of lands in Taiwan. Climate change impacts forests in many aspects and is increasingly likely to undermine the ability of forests to provide basic ecosystem services. To help reduce the impact of climate change on Taiwan’s forests, people must be made aware of the relationship between climate change and forests. Based on questionnaires collected from 17 cities in Taiwan, this study applied spatial analysis to assess the respondents’ understanding of climate change and adaptation strategies for forest management. A total of 650 questionnaires were distributed and 488 valid ones were collected. The results show that (1) Most respondents believe that climate change is true and more than half of the respondents have experienced extreme weather events, especially extreme rainfall; (2) Most respondents believe that climate change will affect Taiwan’s forests with the majority recognizing the increasing impact of extreme events being the primary cause, followed by changes in the composition of tree species and the deterioration of forest adaptability due to climate change; (3) Most respondents expressed that forest management should be adjusted for climate change and called for measures being taken to establish mixed forests as well as monitoring forest damage; (4) In order tomore »address the difficulties faced by forest owners on the impact of climate change, the majority of respondents felt that the government should raise forest owners’ understanding on climate change and adaptation policies, while the subsidy incentives must also be adjusted. The results of this study show that the respondents do realize the importance of climate change and forest management so much so their awareness in this matter led to their support for forest adaptation measures and policies.« less
2. Study on U.S. Parents' Divisions of Labor During COVID-19, Waves 1-2

   https://doi.org/10.3886/E183142V2  

   Carlson, Daniel L. ; Petts, Richard J. ( January 2022 )

   Abstract

   The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally. <br /><br />The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality. <br /><br />Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in October 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through October 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study. <br /><br />SPDLC data will be released to the public two years after data is collected; Waves 1 and 2 are currently publicly available. Wave 3 will be publicly available in October 2023, with subsequent waves becoming available yearly. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available.
   

   Methods

   Response Rates: Of the 1,157 parents who participated in Wave 1, 828 (72%) also participated in the Wave 2 study. Presence of Common Scales: The following established scales are included in the survey:<br /><ul><li><b>Self-Efficacy</b>, adapted from Pearlin&#39;s mastery scale (Pearlin et al., 1981) and the Rosenberg self-esteem scale (Rosenberg, 2015) and taken from the American Changing Lives Survey</li><li><b>Communication with Partner</b>, taken from the Marriage and Relationship Survey (Lichter &amp; Carmalt, 2009)<br /></li><li><b>Gender Attitudes</b>, taken from the National Survey of Families and Households (Sweet &amp; Bumpass, 1996)<br /></li><li><b>Depressive Symptoms </b>(CES-D-10)</li><li><b>Stress</b>, measured using Cohen&#39;s Perceived Stress Scale (Cohen, Kamarck, &amp; Mermelstein, 1983)<br /></li></ul>Full details about these scales and all other items included in the survey can be found in the user guide and codebook<br /> The second wave of the SPDLC was fielded in November 2020 in two stages. In the first stage, all parents who participated in W1 of the SPDLC and who continued to reside in the United States were re-contacted and asked to participate in a follow-up survey. The W2 survey was posted on Prolific, and messages were sent via Prolific’s messaging system to all previous participants. Multiple follow-up messages were sent in an attempt to increase response rates to the follow-up survey. Of the 1,157 respondents who completed the W1 survey, 873 at least started the W2 survey. Data quality checks were employed in line with best practices for online surveys (e.g., removing respondents who did not complete most of the survey or who did not pass the attention filters). After data quality checks, 5.2% of respondents were removed from the sample, resulting in a final sample size of 828 parents (a response rate of 72%). <br /><br />In the second stage, a new sample of parents was recruited. New parents had to meet the same sampling criteria as in W1 (be at least 18 years old, reside in the United States, reside with a romantic partner, and be a parent living with at least one biological child). Also similar to the W1 procedures, we oversampled men, Black individuals, individuals who did not complete college, and individuals who identified as politically conservative to increase sample diversity. A total of 1,207 parents participated in the W2 survey. Data quality checks led to the removal of 5.7% of the respondents, resulting in a final sample size of new respondents at Wave 2 of 1,138 parents. <br /><br />In both stages, participants were informed that the survey would take approximately 20 minutes to complete. All panelists were provided monetary compensation in line with Prolific’s compensation guidelines, which require that all participants earn above minimum wage for their time participating in studies. <br /> To be included in SPDLC, respondents had to meet the following sampling criteria at the time they enter the study: (a) be at least 18 years old, (b) reside in the United States, (c) reside with a romantic partner (i.e., be married or cohabiting), and (d) be a parent living with at least one biological child. Follow-up respondents must be at least 18 years old and reside in the United States, but may experience changes in relationship and resident parent statuses. Smallest Geographic Unit: U.S. State <p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. In accordance with this license, all users of these data must give appropriate credit to the authors in any papers, presentations, books, or other works that use the data. A suggested citation to provide attribution for these data is included below:             <br /><br />Carlson, Daniel L. and Richard J. Petts. 2022. Study on U.S. Parents’ Divisions of Labor During COVID-19 User Guide: Waves 1-2.  </p>
   

   Other

   To help provide estimates that are more representative of U.S. partnered parents, the SPDLC includes sampling weights. Weights can be included in statistical analyses to make estimates from the SPDLC sample representative of U.S. parents who reside with a romantic partner (married or cohabiting) and a child aged 18 or younger based on age, race/ethnicity, and gender. National estimates for the age, racial/ethnic, and gender profile of U.S. partnered parents were obtained using data from the 2020 Current Population Survey (CPS). Weights were calculated using an iterative raking method, such that the full sample in each data file matches the nationally representative CPS data in regard to the gender, age, and racial/ethnic distributions within the data. This variable is labeled <b>CPSweightW2</b> in the Wave 2 dataset, and <b>CPSweightLW2</b> in the longitudinal dataset (which includes Waves 1 and 2). There is not a weight variable included in the W1-W2 repeated cross-section data file.<br />
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3. Defining First-generation and Low-income Students in Engineering: An Exploration

   https://doi.org/10.18260/1-2--34373  

   Atwood, Sara A ; Gilmartin, Shannon K ; Harris, Angela ; Sheppard, Sheri ( January 2020 , ASEE Annual Conference proceedings)

   First-generation (FG) and/or low-income (LI) engineering student populations are of particular interest in engineering education. However, these populations are not defined in a consistent manner across the literature or amongst stakeholders. The intersectional identities of these groups have also not been fully explored in most quantitative-based engineering education research. This research paper aims to answer the following three research questions: (RQ1) How do students’ demographic characteristics and college experiences differ depending on levels of parent educational attainment (which forms the basis of first-generation definitions) and family income? (RQ2) How do ‘first-generation’ and ‘low-income’ definitions impact results comparing to their continuing-generation and higher-income peers? (RQ3) How does considering first-generation and low-income identities through an intersectional lens deepen insight into the experiences of first-generation and low-income groups? Data were drawn from a nationally representative survey of engineering juniors and seniors (n = 6197 from 27 U.S. institutions). Statistical analyses were conducted to evaluate respondent differences in demographics (underrepresented racial/ethnic minority (URM), women, URM women), college experiences (internships/co-ops, having a job, conducting research, and study abroad), and engineering task self-efficacy (ETSE), based on various definitions of ‘first generation’ and ‘low income’ depending on levels of parental educational attainment and self-reported family income. Ourmore »results indicate that categorizing a first-generation student as someone whose parents have less than an associate’s degree versus less than a bachelor’s degree may lead to different understandings of their experiences (RQ1). For example, the proportion of URM students is higher among those whose parents have less than an associate’s degree than among their “associate’s degree or more” peers (26% vs 11.9%). However, differences in college experiences are most pronounced among students whose parents have less than a bachelor’s degree compared with their “bachelor’s degree or more” peers: having a job to help pay for college (55.4% vs 47.3%), research with faculty (22.7% vs 35.0%), and study abroad (9.0% vs 17.3%). With respect to differences by income levels, respondents are statistically different across income groups, with fewer URM students as family income level increases. As family income level increases, there are more women in aggregate, but fewer URM women. College experiences are different for the middle income or higher group (internship 48.4% low and lower-middle income vs 59.0% middle income or higher; study abroad 11.2% vs 16.4%; job 58.6% vs 46.8%). Despite these differences in demographic characteristics and college experiences depending on parental educational attainment and family income, our dataset indicates that the definition does not change the statistical significance when comparing between first-generation students and students who were continuing-generation by any definition (RQ2). First-generation and low-income statuses are often used as proxies for one another, and in this dataset, are highly correlated. However, there are unique patterns at the intersection of these two identities. For the purpose of our RQ3 analysis, we define ‘first-generation’ as students whose parents earned less than a bachelor’s degree and ‘low-income’ as low or lower-middle income. In this sample, 68 percent of students were neither FG nor LI while 11 percent were both (FG&LI). On no measure of demographics or college experience is the FG&LI group statistically similar to the advantaged group. Low-income students had the highest participation in working to pay for college, regardless of parental education, while first-generation students had the lower internship participation than low-income students. Furthermore, being FG&LI is associated with lower ETSE compared with all other groups. These results suggest that care is required when applying the labels “first-generation” and/or “low-income” when considering these groups in developing institutional support programs, in engineering education research, and in educational policy. Moreover, by considering first-generation and low-income students with an intersectional lens, we gain deeper insight into engineering student populations that may reveal potential opportunities and barriers to educational resources and experiences that are an important part of preparation for an engineering career.« less
4. Closing the doors of opportunity: A field theoretic analysis of the prevalence and nature of obstacles to college internships

   https://doi.org/10.1177/01614681211070875  

   Hora, M.T. ( January 2021 , Teachers College record)

   Background: Internships for college students can enhance their grades, skills, and employment prospects, but finding and completing an internship sometimes requires considerable resources. Consequently, before postsecondary institutions consider mandating this high-impact practice, more evidence is needed regarding the various obstacles students face as they seek an internship. Focus of Study: The purpose of this study was to document the prevalence and nature of obstacles to securing a college internship and how these factors interact in the lives of particular students. Field theory is used to highlight the ways that structural inequalities and forms of capital serve to facilitate or constrain access to an internship experience. Population: The participants in this study included students attending five postsecondary institutions—three comprehensive universities, one historically Black college and university (HBCU), and one technical college in the U.S. states of Maryland, South Carolina, and Wisconsin. Research Design: This concurrent mixed-methods study included the collection of survey (n = 1,549) and focus group and interview (n = 100) data from students who self-selected into the study. Given that this is a descriptive study, the aim was to document student experiences with obstacles to internships using varied sources of data. Data Collection and Analysis: Data were collectedmore »via an online survey (with a 26% response rate) and in-person focus groups or interviews at each campus. Data were analyzed using inductive thematic analysis, social network analysis, and logistic regression techniques and interpreted in ways that highlight the situated and critical role of capital and structure in shaping opportunity and behavior. Findings: Among the 1,060 (69%) survey respondents who reported not having had an internship, 638 indicated that they had in fact wanted to pursue an internship but could not because of the need to work, a heavy course load, insufficient positions, and inadequate pay. The role of financial, social, and cultural capital also impacted students differentially depending on their majors, socioeconomic status, race, and geographic location, highlighting how context and enduring systemic forces—and not solely the possession of capital(s)—intersect to shape students’ abilities to pursue an internship. Conclusion: Internships are not universally accessible to all college students and instead favor students who have access to financial, social, and cultural capital while also being positioned in particular majors, geographic locations, and institutions. Before actively promoting internships for their students, colleges and universities should secure funding to support student pay and relocation costs, identify alternative forms of experiential learning for working students, and engage employers in creating more in-person and online positions for students across the disciplines.« less
5. Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

   https://doi.org/10.3390/hydrology8030104  

   Mejia Manrique, Said A. ; Harmsen, Eric W. ; Khanbilvardi, Reza M. ; González, Jorge E. ( September 2021 , Hydrology)

   Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph frommore »the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events.« less