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This article draws on the experience from an ongoing research project employing respondent-driven sampling (RDS) to survey (illicit) 24-hour home care workers. We highlight issues around the preparatory work and the fielding of the survey to provide researchers with useful insights on how to implement RDS when surveying populations for which the method has not yet been used. We conclude the article with ethical considerations that occur when employing RDS.
This paper compares the usability of data stemming from probability sampling with data stemming from nonprobability sampling. It develops six research scenarios that differ in their research goals and assumptions about the data generating process. It is shown that inferences from data stemming from nonprobability sampling implies demanding assumptions on the homogeneity of the units being studied. Researchers who are not willing to pose these assumptions are generally better off using data from probability sampling, regardless of the amount of nonresponse. However, even in cases when data from probability sampling is clearly advertised, data stemming from nonprobability sampling may contribute to the cumulative scientific endeavour of pinpointing a plausible interval for the parameter of interest.
Classical Wolf-Rayet (cWR) stars are at a crucial evolutionary stage for constraining the fates of massive stars. The feedback of these hot, hydrogen-depleted stars dominates their surrounding by tremendous injections of ionizing radiation and kinetic energy. The strength of a Wolf-Rayet (WR) wind decides the eventual mass of its remnant, likely a massive black hole. However, despite their major influence and importance for gravitational wave detection statistics, WR winds are particularly poorly understood. In this paper, we introduce the first set of hydrodynamically consistent stellar atmosphere models for cWR stars of both the carbon (C) and the nitrogen (N) sequence, i.e. WC and WN stars, as a function of stellar luminosity-to-mass ratio (or Eddington Gamma) and metallicity. We demonstrate the inapplicability of the CAK wind theory for cWR stars and confirm earlier findings that their winds are launched at the (hot) iron (Fe) opacity peak. For log Z/Z(circle dot) > -2, Fe is also the main accelerator throughout the wind. Contrasting previous claims of a sharp lower mass-loss limit forWR stars, we obtain a smooth transition to optically thin winds. Furthermore, we find a strong dependence of the mass-loss rates on Eddington Gamma, both at solar and subsolar metallicity. Increases inWCcarbon and oxygen abundances turn out to slightly reduce the predicted mass-loss rates. Calculations at subsolar metallicities indicate that below the metallicity of the Small Magellanic Cloud, WR mass-loss rates decrease much faster than previously assumed, potentially allowing for high black hole masses even in the local Universe.
We examine how and under what conditions informal institutional constraints, such as precedent and doctrine, are likely to affect collective choice within international organisations even in the absence of powerful bureaucratic agents. With a particular focus on the United Nations Security Council, we first develop a theoretical account of why such informal constraints might affect collective decisions even of powerful and strategically behaving actors. We show that precedents provide focal points that allow adopting collective decisions in coordination situations despite diverging preferences. Reliance on previous cases creates tacitly evolving doctrine that may develop incrementally. Council decision-making is also likely to be facilitated by an institutional logic of escalation driven by institutional constraints following from the typically staged response to crisis situations. We explore the usefulness of our theoretical argument with evidence from the Council doctrine on terrorism that has evolved since 1985. The key decisions studied include the 1992 sanctions resolution against Libya and the 2001 Council response to the 9/11 attacks. We conclude that, even within intergovernmentally structured international organisations, member states do not operate on a clean slate, but in a highly institutionalised environment that shapes their opportunities for action.
Professional development on fostering students’ academic language proficiency across the curriculum
(2019)
This meta-analysis aggregates effects from 10 studies evaluating professional development interventions aimed at qualifying in-service teachers to support their students in mastering academic language skills while teaching their respective subject areas. The analysis of a subset of studies revealed a small non-significant weighted training effect on teachers' cognition (g' = 0.21, SE = 0.14). An effect aggregation including all studies (with 650 teachers) revealed a medium to large weighted overall effect on teachers' classroom practices (g' = 0.71, SE = 0.16). Methodological variables moderated the effect magnitude. Nevertheless, the results suggest professional development is beneficial for improving teachers' practice.
Duplicate detection algorithms produce clusters of database records, each cluster representing a single real-world entity. As most of these algorithms use pairwise comparisons, the resulting (transitive) clusters can be inconsistent: Not all records within a cluster are sufficiently similar to be classified as duplicate. Thus, one of many subsequent clustering algorithms can further improve the result. <br /> We explain in detail, compare, and evaluate many of these algorithms and introduce three new clustering algorithms in the specific context of duplicate detection. Two of our three new algorithms use the structure of the input graph to create consistent clusters. Our third algorithm, and many other clustering algorithms, focus on the edge weights, instead. For evaluation, in contrast to related work, we experiment on true real-world datasets, and in addition examine in great detail various pair-selection strategies used in practice. While no overall winner emerges, we are able to identify best approaches for different situations. In scenarios with larger clusters, our proposed algorithm, Extended Maximum Clique Clustering (EMCC), and Markov Clustering show the best results. EMCC especially outperforms Markov Clustering regarding the precision of the results and additionally has the advantage that it can also be used in scenarios where edge weights are not available.
Industry 4.0, based on increasingly progressive digitalization, is a global phenomenon that affects every part of our work. The Internet of Things (IoT) is pushing the process of automation, culminating in the total autonomy of cyber-physical systems. This process is accompanied by a massive amount of data, information, and new dimensions of flexibility. As the amount of available data increases, their specific timeliness decreases. Mastering Industry 4.0 requires humans to master the new dimensions of information and to adapt to relevant ongoing changes. Intentional forgetting can make a difference in this context, as it discards nonprevailing information and actions in favor of prevailing ones. Intentional forgetting is the basis of any adaptation to change, as it ensures that nonprevailing memory items are not retrieved while prevailing ones are retained. This study presents a novel experimental approach that was introduced in a learning factory (the Research and Application Center Industry 4.0) to investigate intentional forgetting as it applies to production routines. In the first experiment (N = 18), in which the participants collectively performed 3046 routine related actions (t1 = 1402, t2 = 1644), the results showed that highly proceduralized actions were more difficult to forget than actions that were less well-learned. Additionally, we found that the quality of cues that trigger the execution of routine actions had no effect on the extent of intentional forgetting.
Selfish Network Creation focuses on modeling real world networks from a game-theoretic point of view. One of the classic models by Fabrikant et al. (2003) is the network creation game, where agents correspond to nodes in a network which buy incident edges for the price of alpha per edge to minimize their total distance to all other nodes. The model is well-studied but still has intriguing open problems. The most famous conjectures state that the price of anarchy is constant for all alpha and that for alpha >= n all equilibrium networks are trees. We introduce a novel technique for analyzing stable networks for high edge-price alpha and employ it to improve on the best known bound for the latter conjecture. In particular we show that for alpha > 4n - 13 all equilibrium networks must be trees, which implies a constant price of anarchy for this range of alpha. Moreover, we also improve the constant upper bound on the price of anarchy for equilibrium trees.