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PRODID;X-RICAL-TZSOURCE=TZINFO:-//com.denhaven2/NONSGML ri_cal gem//EN
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VERSION:2.0
BEGIN:VEVENT
DTEND;VALUE=DATE-TIME:20200522T122000Z
DTSTART;VALUE=DATE-TIME:20200522T115000Z
DTSTAMP;VALUE=DATE-TIME:20200524T213235Z
UID:1e17af80-2495-48da-9f53-de6765499de5@talks.stuts.de
DESCRIPTION:While findings from single studies are limited in external va
 lidity\, synthesizing evidence from multiple studies allows drawing more
  robust conclusions about a phenomenon. Evidence synthesis typically inv
 olves ‚systematic reviews’ which use transparent and objective methods a
 s well as pre-defined appraisal criteria to synthesize all available res
 earch on a research question. This reduces risks of bias that traditiona
 l literature reviews are vulnerable to. Statistical methods may be used 
 to aggregate results from included studies (meta-analysis) or not ('narr
 ative synthesis'\, cf. Gough\, Oliver & Thomas 2017). \n\nIn linguistics
 \, evidence synthesis is used to advance linguistic theory (see Nicenboi
 m\, Roettger & Vasishth 2018 for phonetics\; see van den Noort et al. 20
 19 for bilingualism research)\, potentially inform language policy (Norr
 is & Ortega 2007\; Berthele 2019) and guide clinical practice (e. g. con
 cerning dementia\, see Brini et al. 2020).\n\nCurrent challenges include
  a lack of high-quality primary studies\, redundancy due to duplicate wo
 rk (for an example from linguistics\, cf. Brini et al. 2020)\, shortcomi
 ngs of statistical methods such as meta-analyses (cf. Nicenboim\, Roettg
 er & Vasishth 2018) and keeping reviews up to date in the light of a fas
 ter gowing body of research (cf. Westgate & Lindenmayer 2017).\n\nTechni
 cal solutions from computational linguistics such as semantic networks a
 nd text mining approaches are already used to tackle some of these chall
 enges by automizing labor-intensive stages of systematic reviews (Westga
 te & Lindenmayer 2017). Going further\, Nakagawa et al. (2020) call for 
 a “new ecosystem of evidence synthesis” in which technological solutions
  “promote openness and interconnectedness” (p. 3). They propose all empi
 cists be organized in open synthesis communities where “synthesis is rec
 ognized as the end goal\, as researchers design\, undertake and report t
 heir work” (p. 1). \n\nWe will discuss the benefits of evidence synthesi
 s communities for linguistic research and ask how computational linguist
 ics may advance evidence synthesis methodology.
URL:https://talks.stuts.de/de/stuts67/public/events/266
SUMMARY:Using evidence synthesis in linguistics and computational linguis
 tics in evidence synthesis
ORGANIZER:stuts67
LOCATION:stuts67 - Chomsky
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