Quantitative research and issues of political sensitivity in rural China

Political sensitivity is always a challenge for the scholar doing fieldwork in nondemocratic and transitional systems, especially when doing surveys and quantitative research. Not only are more research topics likely to be politically sensitive in these systems, but in trying to collect precise and unbiased data to give us a quantitative description of a population, we are sometimes doing exactly what the government – and sometimes certain members of that population -- would like to prevent. In this chapter, I discuss some of the methodological and ethical issues that face researchers working in these contexts and describe strategies for dealing with these issues. I argue that in these contexts a “socially embedded” approach to survey research that carefully attends to the social relationships inherent in the survey research process can help alleviate problems of political sensitivity, protect participants and researchers in the survey research process, and maximize data quality

The effect of civic leadership training on citizen engagement and government responsiveness: experimental evidence from the Philippines

What are the effects of providing civic leadership training to community leaders from marginalised groups? Can it lead to increased participation by new leaders in local government processes, and increased government responsiveness to the needs of the poorest and most marginalised? Does it have the unintended consequence of these new leaders being co-opted by local politicians? This research investigated the impact of civic leadership training on citizen participation and government responsiveness in the Philippines. It examined an experimental pilot intervention that targeted ‘parent leaders’ – individuals already identified as community leaders in a large-scale government conditional cash transfer programme that aims to benefit the ‘poorest of the poor’. The research collaboration evaluated the impact of this model on the political participation of parent leaders, and the responsiveness of local government officials to the needs of marginalised groups. In addition, it assessed the potential for unintended political consequences of the leadership training in the Philippines, where strong clientelist networks can influence electoral mobilisation. In particular, it considered the possibility that leadership capacity-building might make parent leaders more attractive to politicians as ‘vote brokers’ – individuals who can deliver the votes of their fellow beneficiaries in exchange for personal gain.DFIDUSAIDSidaOmidyar Networ

Does Information Lead to More Active Citizenship? Evidence from an Education Intervention in Rural Kenya

We study a randomized educational intervention in 550 households in 26 matched villages in two Kenyan districts. The intervention provided parents with information about their children's performance on literacy and numeracy tests, and materials about how to become more involved in improving their children's learning. We find the provision of such information had no discernible impact on either private or collective action. In discussing these findings, we articulate a framework linking information provision to changes in citizens' behavior, and assess the present intervention at each step. Future research on information provision should pay greater attention to this framework. © 2014 Elsevier Ltd

A Pre-mRNA–Associating Factor Links Endogenous siRNAs to Chromatin Regulation

In plants and fungi, small RNAs silence gene expression in the nucleus by establishing repressive chromatin states. The role of endogenous small RNAs in metazoan nuclei is largely unknown. Here we show that endogenous small interfering RNAs (endo-siRNAs) direct Histone H3 Lysine 9 methylation (H3K9me) in Caenorhabditis elegans. In addition, we report the identification and characterization of nuclear RNAi defective (nrde)-1 and nrde-4. Endo-siRNA–driven H3K9me requires the nuclear RNAi pathway including the Argonaute (Ago) NRDE-3, the conserved nuclear RNAi factor NRDE-2, as well as NRDE-1 and NRDE-4. Small RNAs direct NRDE-1 to associate with the pre-mRNA and chromatin of genes, which have been targeted by RNAi. NRDE-3 and NRDE-2 are required for the association of NRDE-1 with pre-mRNA and chromatin. NRDE-4 is required for NRDE-1/chromatin association, but not NRDE-1/pre-mRNA association. These data establish that NRDE-1 is a novel pre-mRNA and chromatin-associating factor that links small RNAs to H3K9 methylation. In addition, these results demonstrate that endo-siRNAs direct chromatin modifications via the Nrde pathway in C. elegans

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages