可检索但未被遇见：大型学术电子书馆藏中缺失的曝光分母
Retrievable but Unencountered: The Missing Exposure Denominator in Large Academic Ebook Collections
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中文总结 AI 辅助
该研究区分了可检索性与可遇见性，发现大型学术电子书馆藏存在使用数据偏差问题，提出四个测量方向以缩小反馈缺失带来的识别差距。
中文摘要 AI 辅助
学术图书馆的电子书馆藏规模庞大，没有读者能查看目录中可忽略不计的一小部分，对这些馆藏的评估几乎完全依赖使用遥测数据。一本没有记录交互的图书被视为无人想要的图书，但现有证据无法支持这一推断。我们区分了文献中混淆的两个概念：可检索性（系统对已明确指定的查询返回某一项目的概率）和可遇见性（某一项目在无预先标题级意图的情况下进入读者注意力的概率）。按照既定协议对2018年及以后发表的著作进行研究，我们发现三点：大型零使用群体得到充分记录；使用情况在很大程度上取决于测量工具；没有标准图书馆指标记录项目印象。结果是存在识别问题，记录的非使用混合了未曝光、未被注意的曝光、拒绝和非渠道使用，且根据我们的协议，没有研究对其进行分解。推荐系统研究已将此问题命名：那里的反馈不是完全随机缺失的。我们最后提出四个测量方向，可缩小差距但无法消除差距。
英文摘要
Academic libraries hold ebook collections so large that no reader can inspect more than a negligible fraction of the catalogue. Assessment of those collections rests almost entirely on usage telemetry. A title that generates no recorded interaction is treated as a title nobody wanted. That inference is unavailable on the present evidence. We separate two constructs the literature conflates: retrievability, the probability that a system returns an item to a query that already specifies it, and encounterability, the probability that an item enters a reader's attention without prior title-level intent. Following a stated protocol over work published from 2018 onward, we find three things. Large zero-use populations are robustly documented. Usage depends heavily on the measuring instrument. And no standard library metric records an item impression. The consequence is an identification problem. Recorded non-use mixes non-exposure, unnoticed exposure, rejection, and off-channel use, and no study located under our protocol decomposes it. Recommender-system research already names this problem: there, feedback is missing not at random. We close with four measurement directions that would narrow the gap without closing it.
