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1. 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 , ICPSR - Interuniversity Consortium for Political and Social Research)

   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.
   
   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.
   
   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.
   
   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. 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:
   

   • Self-Efficacy, adapted from Pearlin's mastery scale (Pearlin et al., 1981) and the Rosenberg self-esteem scale (Rosenberg, 2015) and taken from the American Changing Lives Survey
   • Communication with Partner, taken from the Marriage and Relationship Survey (Lichter & Carmalt, 2009)
     
   • Gender Attitudes, taken from the National Survey of Families and Households (Sweet & Bumpass, 1996)
     
   • Depressive Symptoms (CES-D-10)
   • Stress, measured using Cohen's Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983)
     

   Full details about these scales and all other items included in the survey can be found in the user guide and codebook
   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%).
   
   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.
   
   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.
   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

   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:            
   
   Carlson, Daniel L. and Richard J. Petts. 2022. Study on U.S. Parents’ Divisions of Labor During COVID-19 User Guide: Waves 1-2.  

   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 CPSweightW2 in the Wave 2 dataset, and CPSweightLW2 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.
    

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2. Species diversification and phylogenetically constrained symbiont switching generated high modularity in the lichen genus Peltigera

   https://doi.org/10.1111/1365-2745.13207  

   Chagnon, Pierre‐Luc ; Magain, Nicolas ; Miadlikowska, Jolanta ; Lutzoni, Francois ; Shefferson, ed., Richard ( May 2019 , Journal of Ecology)

   Ecological interactions range from purely specialized to extremely generalized in nature. Recent research has showed very high levels of specialization in the cyanolichens involvingPeltigera(mycobionts) and theirNostocphotosynthetic partners (cyanobionts). Yet, little is known about the mechanisms contributing to the establishment and maintenance of such high specialization levels.

   Here, we characterized interactions betweenPeltigeraandNostocpartners at a global scale, using more than one thousand thalli. We used tools from network theory, community phylogenetics and biogeographical history reconstruction to evaluate how these symbiotic interactions may have evolved.

   After splitting the interaction matrix into modules of preferentially interacting partners, we evaluated how module membership might have evolved along the mycobionts’ phylogeny. We also teased apart the contributions of geographical overlap vs phylogeny in driving interaction establishment betweenPeltigeraandNostoctaxa.

   Module affiliation rarely evolves through the splitting of large ancestral modules. Instead, new modules appear to emerge independently, which is often associated with a fungal speciation event. We also found strong phylogenetic signal in these interactions, which suggests that partner switching is constrained by conserved traits. Therefore, it seems that a high rate of fungal diversification following a switch to a new cyanobiont can lead to the formation of large modules, with cyanobionts associating with multiple closely retatedPeltigeraspecies.

   Finally, when restricting our analyses toPeltigerasister species, the latter differed more through partner acquisition/loss than replacement (i.e., switching). This pattern vanishes as we look at sister species that have diverged longer ago. This suggests that fungal speciation may be accompanied by a stepwise process of (a) novel partner acquisition and (b) loss of the ancestral partner. This could explain the maintenance of high specialization levels in this symbiotic system where the transmission of the cyanobiont to the next generation is assumed to be predominantly horizontal.

   Synthesis.Overall, our study suggests that oscillation between generalization and ancestral partner loss may maintain high specialization within the lichen genusPeltigera, and that partner selection is not only driven by partners’ geographical overlap, but also by their phylogenetically conserved traits.

    

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3. Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue

   Bara, Cristian-Paul ; M, Ziqiao ; Yu, Yingzhuo ; Shah, Julie ; Chai, Joyce ( August 2023 , 2023 International Joint Conferences on Artificial Intelligence)

   Collaborative tasks often begin with partial task knowledge and incomplete initial plans from each partner. To complete these tasks, agents need to engage in situated communication with their partners and coordinate their partial plans towards a complete plan to achieve a joint task goal. While such collaboration seems effortless in a human-human team, it is highly challenging for human-AI collaboration. To address this limitation, this paper takes a step towards collaborative plan acquisition, where humans and agents strive to learn and communicate with each other to acquire a complete plan for joint tasks. Specifically, we formulate a novel problem for agents to predict the missing task knowledge for themselves and for their partners based on rich perceptual and dialogue history. We extend a situated dialogue benchmark for symmetric collaborative tasks in a 3D blocks world and investigate computational strategies for plan acquisition. Our empirical results suggest that predicting the partner's missing knowledge is a more viable approach than predicting one's own. We show that explicit modeling of the partner's dialogue moves and mental states produces improved and more stable results than without. These results provide insight for future AI agents that can predict what knowledge their partner is missing and, therefore, can proactively communicate such information to help their partner acquire such missing knowledge toward a common understanding of joint tasks. 

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4. Audience Design in Multiparty Conversation

   https://doi.org/10.1111/cogs.12774  

   Yoon, Si On ; Brown‐Schmidt, Sarah ( August 2019 , Cognitive Science)

   How do speakers design what they say in order to communicate effectively with groups of addressees who vary in their background knowledge of the topic at hand? Prior findings indicate that when a speaker addresses apairof listeners with discrepant knowledge, that speakersAim Low, designing their utterances for the least knowledgeable of the two addressees. Here, we test the hypothesis that speakers will depart from anAim Lowapproach in order to efficiently communicate with larger groups of interacting partners. Further, we ask whether the cognitive demands of tracking multiple conversational partners' perspectives places limitations on successful audience design. We find that speakers can successfully track information about what up to four of their partners do and do not know in conversation. When addressing groups of 3–4 addressees at once, speakers design language based on the combined knowledge of the group. These findings point to an audience design process that simultaneously represents the perspectives of multiple other individuals and combines these representations in order to design utterances that strike a balance between the different needs of the individuals within the group.

    

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5. Retrieval processes and audience design

   https://doi.org/10.1016/j.jml.2020.104149  

   Ahn, S. Brown-Schmidt ( December 2020 , Journal of memory and language)

   null (Ed.)

   Conversational partners develop shared knowledge. In referential communication tasks, partners collaboratively establish brief labels for hard-to-name images. These image-label mappings are associated in memory with that partner, evidenced by use of those brief labels with the same partner, and longer descriptions with new partners. According to the people-as-contexts view, the conversational partner functions as a contextual cue to support retrieval of conversationally-relevant information. Inspired by findings from the memory literature that context effects can be stronger when retrieval is more explicit, two experiments test the hypothesis that the speaker will be more likely to invoke the partner as a retrieval cue when retrieval processes are more explicit. The results indicated a strong effect of partner that, contrary to these predictions, was not boosted by explicit retrieval processes. The lack of an effect of retrieval processes speaks to the ubiquity with which language use in conversation is tailored to the particular people with whom we converse. 

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