Westgate Martin J, Lindenmayer David B
Fenner School of Environment and Society, The Australian National University, Canberra, ACT, 2601, Australia.
ARC Centre of Excellence for Environmental Decisions, The Australian National University, Canberra, ACT, 2601, Australia.
Conserv Biol. 2017 Oct;31(5):1002-1007. doi: 10.1111/cobi.12890. Epub 2017 Jun 20.
The need for robust evidence to support conservation actions has driven the adoption of systematic approaches to research synthesis in ecology. However, applying systematic review to complex or open questions remains challenging, and this task is becoming more difficult as the quantity of scientific literature increases. We drew on the science of linguistics for guidance as to why the process of identifying and sorting information during systematic review remains so labor intensive, and to provide potential solutions. Several linguistic properties of peer-reviewed corpora-including nonrandom selection of review topics, small-world properties of semantic networks, and spatiotemporal variation in word meaning-greatly increase the effort needed to complete the systematic review process. Conversely, the resolution of these semantic complexities is a common motivation for narrative reviews, but this process is rarely enacted with the rigor applied during linguistic analysis. Therefore, linguistics provides a unifying framework for understanding some key challenges of systematic review and highlights 2 useful directions for future research. First, in cases where semantic complexity generates barriers to synthesis, ecologists should consider drawing on existing methods-such as natural language processing or the construction of research thesauri and ontologies-that provide tools for mapping and resolving that complexity. These tools could help individual researchers classify research material in a more robust manner and provide valuable guidance for future researchers on that topic. Second, a linguistic perspective highlights that scientific writing is a rich resource worthy of detailed study, an observation that can sometimes be lost during the search for data during systematic review or meta-analysis. For example, mapping semantic networks can reveal redundancy and complementarity among scientific concepts, leading to new insights and research questions. Consequently, wider adoption of linguistic approaches may facilitate improved rigor and richness in research synthesis.
支持保护行动需要有力证据，这推动了生态学研究综合采用系统方法。然而，将系统评价应用于复杂或开放性问题仍具有挑战性，而且随着科学文献数量的增加，这项任务正变得愈发困难。我们借鉴语言学的科学知识，以了解为何在系统评价中识别和分类信息的过程仍如此耗费人力，并提供潜在的解决方案。同行评审语料库的几个语言特性——包括综述主题的非随机选择、语义网络的小世界特性以及词义的时空变化——极大地增加了完成系统评价过程所需的工作量。相反，解决这些语义复杂性问题是叙述性综述的常见动机，但这个过程很少像语言分析那样严格执行。因此，语言学为理解系统评价的一些关键挑战提供了一个统一框架，并突出了未来研究的两个有用方向。首先，在语义复杂性对综合产生障碍的情况下，生态学家应考虑借鉴现有方法，如自然语言处理或构建研究词库和本体，这些方法提供了用于映射和解决这种复杂性的工具。这些工具可以帮助个体研究人员更有力地对研究材料进行分类，并为该主题的未来研究人员提供有价值的指导。其次，语言学视角强调科学写作是一个值得详细研究的丰富资源，而这一观察结果在系统评价或荟萃分析的数据搜索过程中有时会被忽视。例如，绘制语义网络可以揭示科学概念之间的冗余和互补性，从而带来新的见解和研究问题。因此，更广泛地采用语言学方法可能有助于提高研究综合的严谨性和丰富性。
