Exploring dialogic strategies in social media for fostering citizens’ interactions with Latin American local governments

Although the benefits of social media for enhancing citizens’ interactions with their local governments are well known in a more global context, few studies have focused on Latin America, or on the MERCOSUR countries in particular. This paper examines the use of dialogic strategies in the social media activities of local governments in MERCOSUR countries in order to generate citizens’ online engagement. Analysis of this question enhances our understanding of the use of social media by local governments in less developed countries and highlights the need for empirical evidence on the use of dialogic communication theory regarding stakeholder engagement with social media. The findings obtained reflect the positive influence of dialogic strategies on citizens’ online engagement, particularly those based on dialogic loop, useful information and generation of return visits, aimed at fostering participation via social media.     

Standards of Care in Assessment of Lesbian,Gay, Bisexual,Transgender, Gender Expansive,and Queer/Questioning (LGBTGEQ+) Persons

ABSTRACT

A Joint Task Force comprised of members of the Association for Lesbian, Gay, Bisexual, and Transgender Issues in Counseling (ALGBTIC) and the Association for Assessment and Research in Counseling (AARC) present standards of care for assessment of Lesbian, Gay, Bisexual, Transgender, Gender Expansive, and Queer/Questioning (LGBTGEQ+) Persons.     

Proposing an Initiative Research Methodology for LGBTQ+ Youth: Photo-Elicitation and Thematic Analysis

ABSTRACT

A framework for approaching lesbian, gay, bisexual, transgender, queer+ (LGBTQ+) youth is presented that honors their voice. This methodology utilizes participant-driven photo elicitation as a qualitative data collection method, and then integrates thematic analysis. A brief review of current literature is provided to outline gaps, which establish the need to expand effective research methods with LGBTQ+ youth. Photo elicitation is explained with discussion specific to use with LGBTQ+ youth. Thematic analysis is identified and explained as an analytic approach. Challenges of implementing this methodology are discussed with attention to ethical concerns.     

Modified linear programming and class 0 bounds for graph pebbling

Given a configuration of pebbles on the vertices of a connected graph G, a pebbling move removes two pebbles from some vertex and places one pebble on an adjacent vertex. The pebbling number of a graph G is the smallest integer k such that for each vertex v and each configuration of k pebbles on G there is a sequence of pebbling moves that places at least one pebble on v. First, we improve on results of Hurlbert, who introduced a linear optimization technique for graph pebbling. In particular, we use a different set of weight functions, based on graphs more general than trees. We apply this new idea to some graphs from Hurlbert’s paper to give improved bounds on their pebbling numbers. Second, we investigate the structure of Class 0 graphs with few edges. We show that every n-vertex Class 0 graph has at least \(\frac{5}{3}n - \frac{11}{3}\) edges. This disproves a conjecture of Blasiak et al. For diameter 2 graphs, we strengthen this lower bound to \(2n - 5\), which is best possible. Further, we characterize the graphs where the bound holds with equality and extend the argument to obtain an identical bound for diameter 2 graphs with no cut-vertex.     

A study on imbalance support vector machine algorithms for sufficient dimension reduction

Li et al. (2011 Li, B., Artemiou, A., Li, L. (2011). Principal support vector machine for linear and nonlinear sufficient dimension reduction. Ann. Stat. 39:3182–3210.[Crossref], [Web of Science ®] , [Google Scholar]) presented the novel idea of using support vector machines (SVMs) to perform sufficient dimension reduction. In this work, we investigate the potential improvement in recovering the dimension reduction subspace when one changes the SVM algorithm to treat imbalance based on several proposals in the machine learning literature. We find out that in most situations, treating the imbalanced nature of the slices will help improve the estimation. Our results are verified through simulation and real data applications.     

The education of children in care and children in need: Who falls behind and when?

We seek to explain the development of the educational gap between children in “out‐of‐home care” (CLA), children deemed in social need (CIN), and other pupils. A cohort of 642,805 pupils aged 16 in 2013 was used to chart the educational progress of the full cohort, the CLA (n = 6,236), the CIN in 2012 or 2013 but not CLA (n = 20,384), and a sample individually matched with the CLA (n = 11,084). At age 7, attainment of the CLA and CIN was approximately 1 standard deviation lower than the cohort average and predicted attainment at 16. At this point, the persistent “CIN” (those with earlier and persistent needs) had the lowest attainment relative to others, and this declined further during secondary school. Those entering care before or during primary school had very low attainment at age 7, but their relative attainment did not decline. Attainment of CLA and CIN at age 16 likely reflects early environment, special educational needs, and poor relationships with secondary school. Policy, research, and intervention should focus on CIN as well as CLA, do so before entry to care, and take account of the onset of, and probable reasons for, educational difficulties.     

A qualitative exploration of reentry service needs: The case of fathers returning from prison

The challenge of community and family reentry after a parent's release from prison remains an under‐addressed area of collateral damage stemming from high rates of U.S. incarceration. Many fathers released from prison return to living with family, and later attribute family connections and parent–child contact as key factors in their postrelease success. However, reentry planning is hampered by a dearth of research on family‐focused reentry services, and consequently, often omits attention to resuming family and parenting roles. To address this gap, we conducted 38 semi‐structured interviews with 19 previously incarcerated fathers, 9 co‐parenting mothers, and 10 relatives to explore service needs of fathers during reentry. Findings suggest programs not only should be multimodal, emphasizing family connections complemented by socioeconomic, self‐care, and social support services, but also should be accessible and relatable, offered within the community, and engaging for fathers and family members. Findings reinforce the importance of self‐determination and human agency while underscoring the multiple challenges fathers face upon reentry. By including the voices of those most affected by incarceration, this study advances knowledge to shape reentry programs and policies, contributes to efforts addressing criminal justice inequities, and promotes well‐being among formerly incarcerated parents and their families.     

Randomization Inference for Outcomes with Clumping at Zero

While randomization inference is well developed for continuous and binary outcomes, there has been comparatively little work for outcomes with nonnegative support and clumping at zero. Typically, outcomes of this type have been modeled using parametric models that impose strong distributional assumptions. This article proposes new randomization inference procedures for nonnegative outcomes with clumping at zero. Instead of making distributional assumptions, we propose various assumptions about the nature of the response to treatment and use permutation inference for both testing and estimation. This approach allows for some natural goodness-of-fit tests for model assessment, as well as flexibility in selecting test statistics sensitive to different potential alternatives. We illustrate our approach using two randomized trials, where job training interventions were designed to increase earnings of participants.     

Model‐free causal inference of binary experimental data

For binary experimental data, we discuss randomization‐based inferential procedures that do not need to invoke any modeling assumptions. In addition to the classical method of moments, we also introduce model‐free likelihood and Bayesian methods based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have some properties superior to moment‐based ones such as only giving estimates in regions of feasible support. Due to the lack of identification of the causal model, we also propose a sensitivity analysis approach that allows for the characterization of the impact of the association between the potential outcomes on statistical inference.