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The American Psychological Association emphasizes the need to talk about all people with inclusivity and respect. Writers using APA Style must strive to use language that is free of bias and avoid perpetuating prejudicial beliefs or demeaning attitudes in their writing. Just as you have learned to check what you write for spelling, grammar, and wordiness, practice reading your work for bias.
communicate what you mean book free 149
105. Carlo was well aware that the whole apparatus of communications, advertising and social networking can be used to lull us, to make us addicted to consumerism and buying the latest thing on the market, obsessed with our free time, caught up in negativity. Yet he knew how to use the new communications technology to transmit the Gospel, to communicate values and beauty.
232. Similarly, especially in the case of young people who do not come from Christian families or institutions, and are slowly growing to maturity, we have to encourage all the good that we can. Christ warned us not to see only the good grain (cf. Mt 13:24-30). At times, in the attempt to develop a pure and perfect youth ministry, marked by abstract ideas, protected from the world and free of every flaw, we can turn the Gospel into a dull, meaningless and unattractive proposition. Such a youth ministry ends up completely removed from the world of young people and suited only to an elite Christian youth that sees itself as different, while living in an empty and unproductive isolation. In rejecting the weeds, we also uproot or choke any number of shoots trying to spring up in spite of their limitations.
295. In this way, discernment becomes a genuine means of spiritual combat, helping us to follow the Lord more faithfully. The desire to know our personal vocation thus takes on a supreme intensity, a different quality and higher level, one that better respects the dignity of our person and our life. In the end, good discernment is a path of freedom that brings to full fruit what is unique in each person, something so personal that only God knows it. Others cannot fully understand or predict from the outside how it will develop.
This approach for evaluating evidence for scale-free structure has several advantages. It provides a systematic procedure applicable to any network data set, and treats every data set equivalently. It provides an evaluation of the scale-free hypothesis over a maximally broad variety of networks, which facilitates the characterization of their empirical ubiquity. And, it provides a means to assess different kinds of evidence for scale-free structure, by combining results from multiple degree distributions, if available in a network data set. The graph-simplification process or the particular evidence criteria used may also introduce biases into the results. We control for these possibilities by considering alternative criteria under multiple robustness analyses.
The balance of evidence for or against scale-free structure does vary by network domain (Fig. 5). These variations provide a means to check the robustness of our results, and can inform future efforts to develop new structural mechanisms. We focus our domain-specific analysis on networks from biological, social, and technological sources (91% of the corpus).
In contrast, social networks present a different picture. Like the corpus overall, half of social networks lack any direct or indirect evidence of scale-free structure (50% Not Scale Free; Fig. 5b), while indirect evidence is slightly less prevalent (41% Super-Weak). The former group includes the Facebook100 online social networks, and the latter includes many Norwegian board of director networks.
Moment ratio scaling. For 3662 degree sequences, the empirical ratio of the second to first moments \(\langle k^2\rangle /\langle k\rangle ^2\) as a function of network size n, showing substantial variation across networks and domains, little evidence of the divergence pattern expected for scale-free distributions, and perhaps a roughly sublinear scaling relationship (smoothed mean via exponential kernel, with smoothed standard deviations)
The structural diversity of real-world networks uncovered here presents both a puzzle and an opportunity. The strong focus in the scientific literature on explaining and exploiting scale-free patterns has meant relatively less is known about mechanisms that produce non-scale-free structural patterns, e.g., those with degree distributions better fitted by a log-normal. Two important directions of future work will be the development and validation of novel mechanisms for generating more realistic degree structure in networks, and novel statistical techniques for identifying or untangling them given empirical data. Similarly, theoretical results concerning the behavior of dynamical processes running on top of networks, including spreading processes like epidemiological models, social influence models, or models of synchronization, may need to be reassessed in light of the genuine structural diversity of real-world networks.
The statistical methods and evidence categories developed and used in our evaluation of the scale-free hypothesis provide a quantitatively rigorous means by which to assess the degree to which some network exhibits scale-free structure. Their application to a novel network data set should enable future researchers to determine whether assuming scale-free structure is empirically justified.
Prior to analysis, each network data set is transformed into one or more graphs, whose degree sequences can be unambiguously tested for a scale-free pattern (for example, Supplementary Fig. 1). For each non-simple graph property of a network, a specific transformation is applied that increases the number of graphs in the data set while removing the given graph property. Full details of this process are given in Supplementary Note 1, and Supplementary Fig. 2. Complicated network data sets can produce a combinatoric number of simple graphs under this process. Treating every simplified degree sequence independently could lead to skewed results, e.g., if a few non-scale-free data sets account for a large fraction of the total extracted simple graphs. To avoid this bias, results are reported at the level of network data sets. Additionally, we require that simplified graphs are neither too sparse nor too dense to be potentially scale free and thus retain for analysis only simplified graphs with mean degree \(2 \ 350c69d7ab