Echo Chambers in Science? The Impact of Academic Recommender Systems on the Dissemination of Scientific Knowledge

科学文献的数字化经历了两个阶段：第一，科学论文开始数字化，第二，出现了谷歌学者、JSTOR和Web of Science等综合搜索引擎，使学者能够快速搜索和定位已发表的研究。大多数科学家现在通过在线搜索引擎和数字图书馆获取文献，很少有科学家走到图书馆，仔细阅读期刊书架上的新论文。因此，这些新的算法搜索引擎为学者们提供了一个新的视角来看待已发表的科学文献，该项目旨在更好地理解这些技术变化对科学发现和信息传播实践的影响。该项目调查的是，这些新工具是否正在增加对更广泛先前文献的获取，从而使科学民主化，或者是将学者的注意力集中在越来越少的一组“明星”论文上。如果新工具真的使最广泛的科学文献更容易获得，它肯定会对科学发现的前景产生影响，因为科学家能够考虑并参与所有相关的先前工作，这是高质量科学活动的关键因素。另一方面，来自其他背景的证据表明，海量信息的可获得性给搜索和过滤过程带来了新的压力。如果这发生在科学领域，这可能意味着科学家越来越依赖出现在搜索结果顶部的更集中的论文子集，从而在科学中创造一个回音室，对科学事业产生意想不到的影响，并对科学创新产生潜在的下游负面影响。这项研究主要通过对JSTOR和科学网的综合引用数据进行统计分析来解决这些关键问题。这项研究的重点是学科内和学科间的引文集中程度；以及期刊在基于文章的搜索环境中的作用。在数字化转型之后，引文模式发生了巨大变化，变得更加集中，更容易受到累积优势过程的影响。通过描述科学家在其科学实践过程中实际如何与信息技术和学术推荐系统互动的观测数据，扩大了人口层面的分析。这种方法的组合随着时间的推移将个人和群体层面的过程联系在一起，并强调了信息检索和引用对科学创新和职业发展轨迹的下游影响。与政策相关的一个关键问题是，在线推荐系统可能会对搜索结果产生偏见，从而影响具体科学发现的可见性。这与使用这些搜索环境的下一代科学家以及评估科学家和他们的工作的人有关。

Digitization of the scientific literauture has occurred in two phases: first, scientific papers began to be digitized, and second, comprehensive search engines such as Google Scholar, JSTOR and Web of Science emerged that allow scholars to quickly search for and locate published research. Most scientists now access the literature through online search engines and digital libraries, and rare is the scientist who walks to the library and peruses the journal shelves for new papers. These new algorithmic search engines thus provide scholars with a new lens into the published scientific literature, and this project aims to better understand the implications of these technological changes on the practice of scientific discovery and information dissemination. The project investigates whether these new tools are increasing access to a wider range of prior literature and thereby democratizing science, or instead concentrating scholars' gaze onto an ever smaller set of "star" papers. If the new tools are truly making the widest range of scientific literature more accessible, it certainly has implications for the prospects for scientific discovery, since scientists are able to consider and engage with all relevant prior work, a critical ingredient for high quality scientific activity. On the other hand, evidence from other contexts suggests that the availability of massive amounts of information puts new pressure on searching and filtering processes. If this occurs in science it could mean that scientists increasingly rely upon more concentrated subset of papers that appear at the top of search results, thereby creating an echo chamber in science with unintended effects on scientific careers and potentially negative downstream impacts on scientific innovation.This research addresses these critical questions primarily via statistical analyses of comprehensive citation data from JSTOR and the Web of Science. The research focuses on the extent of citation concentration within and across disciplines; and the role of journals in an article-based search environment. Citation patterns have changed dramatically in the wake of the digital transition, becoming both more concentrated and more vulnerable to cumulative advantage processes. The population-level analyses are augmented by observational data describing how scientists actually interact with information technologies and academic recommender systems in the course of their scientific practice. This combination of methods links processes at the individual and population levels over time, and emphasizes the downstream impacts of information retrieval and citation on scientific innovation and career trajectories. A critical policy related implication is that online recommender systems can bias search results and thus the visibility of specific scientific findings. This is relevant to the next generation of scientists using these search environments and those who evaluate scientists and their work.