Uncertainty in fertility intentions is a major obstacle to understanding contemporary trends in fertility decision-making and its outcomes. Quantifying this uncertainty by structural factors such as income, ethnicity, and housing conditions is recognized as insufficient. A recently proposed framework on subjective narratives has opened up a new way to gauge factors behind fertility decision-making and uncertainty. Through surveys, such narratives can be elicited with open-ended questions (OEQs). However, analyzing answers to OEQs typically involves extensive human coding, imposing constraints on sample size. Natural Language Processing (NLP) techniques assist researchers in grasping aspects of the underlying reasoning behind responses with much less human effort. In this study, using automatic neural topic modeling methods, we identify and interpret topics and themes underlying the narratives on fertility intention uncertainty of women in the Netherlands. We used Contextualized Topic Models (CTMs), a neural topic model using pre-trained representations of Dutch language, to conduct our analyses. Our results show that nine topics dominate the narratives about fertility planning, with age and health-related issues as the most prominent ones. In addition, we found that uncertainty in fertility intentions is not homogeneous, as women who feel uncertain due to real-life constraints and those who have no fertility plans at all put their stress on vastly different narratives.

中文翻译：

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理解荷兰妇女生育意愿不确定性的叙述：神经主题建模方法


生育意愿的不确定性是理解当代生育决策趋势及其结果的主要障碍。通过收入、种族和住房条件等结构性因素来量化这种不确定性被认为是不够的。最近提出的主观叙事框架开辟了一种衡量生育决策和不确定性背后因素的新方法。通过调查，可以通过开放式问题（OEQ）来引出此类叙述。然而，分析 OEQ 的答案通常涉及大量的人工编码，从而对样本大小施加限制。自然语言处理 (NLP) 技术可帮助研究人员以更少的人力来掌握响应背后的潜在推理。在这项研究中，我们使用自动神经主题建模方法，识别并解释了荷兰女性生育意愿不确定性叙述背后的主题和主题。我们使用情境化主题模型 (CTM) 来进行分析，这是一种使用预先训练的荷兰语表示形式的神经主题模型。我们的结果显示，九个主题主导了有关生育计划的叙述，其中与年龄和健康相关的问题是最突出的。此外，我们发现生育意愿的不确定性并不相同，因为由于现实生活的限制而感到不确定的女性和根本没有生育计划的女性将压力放在截然不同的叙述上。