Autism Research Paper Abstract

Autism Research Paper Abstract-55
This article departs from this approach, and instead, objectively profiles the evolution and diversity of Autism research with a focus on self-emerging patterns of term association (rather than a priori enforced heuristics).Modularity techniques and graph theory visualization provide a comprehensive, accessible overview of self-identified trends in research focus.The nosology and epidemiology of Autism has undergone transformation following consolidation of once disparate disorders under the umbrella diagnostic, autism spectrum disorders.

This article departs from this approach, and instead, objectively profiles the evolution and diversity of Autism research with a focus on self-emerging patterns of term association (rather than a priori enforced heuristics).Modularity techniques and graph theory visualization provide a comprehensive, accessible overview of self-identified trends in research focus.

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This information was formatted and processed within R software (R Core Team, 2016) using functionality within the ‘tm’ and ‘Bibliometric and Co-citation Analysis’ packages (Aria and Cuccurullo, 2017).

This reconceptualization was completed with the publication of the DSM-5, which marked the consolidation of previous diagnostic terminology (American Psychiatric Association, 2013; see Figure 1A).

However, the amalgamation of formerly heterogeneous disorders under a single diagnostic term was, and arguably continues to be, contentious (Singh, 2011).

While underlying association indices are utilized to encapsulate thematic frequency, user manipulation is a feature often adopted in modern bibliometric analysis—from manual semantic coding of core terms to drive subsequent clustering and group identification (e.g., Coulter et al., 1996; Topalli and Ivanaj, 2016; Williams et al., 2016; Isenberg et al., 2017), to the manual manipulation and reduction of large-scale data (Chuang et al., 2013), through to the use of arbitrary thresholds for algorithmic calibration to specify term and group selection (Callon et al., 1991; Coulter et al., 1996; Zhang et al., 2015; Williams et al., 2016).

Enforcing a priori user-specified heuristics for core elements, such as the number of concepts or keywords that constitute a theme and the number of themes to be identified, artificially constrains, and shapes subsequent clustering.

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