科学人才流失模式：OECD国家研究格局的定量分析及方法论考量

doi:10.16382/j.cnki.1000-5560.2025.08.003

摘要/Abstract

摘要：

采用基于队列的纵向研究设计，借助Scopus全球文献计量数据库，追踪分析了38个OECD国家科研人员截至2022年的科研发表数据，探讨了科学界成员是如何离开学术界的，以及科学人才流失在性别、学科领域和时间上的差异。研究涉及2000年（N=142,776）和2010年（N=232,843）开始发表论文的两批科研人员，覆盖科学、技术、工程、数学与医学（STEMM）领域的16个学科，以停止发表论文作为科研人员离开学术界的界定标准。研究显示：初次发表论文十年后，约50%的研究者仍保持学术活跃状态；当追踪周期延长至十九年后，该比例降至30%。通过生存分析法比较男女科学家的流失差异发现，随着女性在科学领域及同批次科研人员中占比的增加，人才流失的性别差异逐渐弱化。学科维度分析揭示，除了所有STEMM领域的整体变化外，具体学科层面的细微变化广泛存在。不同学科在科学人才流失上呈现出不同的性别差异；不同时间进入科学界的科研人员在流失概率上也存在差别。本研究验证了全球文献计量数据库在分析科学人才流失中的应用价值，并指出了原始结构化数据在学术职业的性别、年龄和学科等研究中的方法挑战和局限性。

关键词: 学术生涯, 科研流失, 大数据, 生存分析, 科学学

Abstract:

In this paper, we explore how members of the scientific community leave academic science and how attrition (defined as ceasing to publish) differs across genders, academic disciplines, and over time. Our approach is cohort-based and longitudinal. We track individual male and female scientists over time and quantify the phenomenon traditionally referred to as “leaving science.” Using publication metadata from Scopus—a global bibliometric database of publications and citations—we follow the details of the publishing careers of scientists from 38 OECD countries who started publishing in 2000 (N = 142,776) and 2010 (N = 232,843). Within a decade, about 50% of scientists stay in science and after 19 years – only about 30%. Our study is restricted to 16 STEMM disciplines (science, technology, engineering, mathematics, and medicine), and we track the individual scholarly output of the two cohorts until 2022. We use survival analysis to compare attrition of men and women scientists. With more women in science and more women within cohorts, attrition is becoming ever less gendered. In addition to the combined aggregated changes at the level of all STEMM disciplines, widely nuanced changes were found to occur at the discipline level and over time. Attrition in science means different things for men versus women depending on the discipline; moreover, it means different things for scientists from different cohorts entering the scientific workforce. Finally, global bibliometric datasets were tested in the current study, opening new opportunities to explore gender and disciplinary differences in attrition. It also describes the methodological challenges and research limitations of using raw structured big data in academic career research, such as identifying gender, age, and discipline.

Key words: academic careers, leaving science, big data, survival analysis, science of science

马瑞克·科维克（Marek Kwiek）, 卢卡斯·希穆拉（Lukasz Szymula）. 科学人才流失模式：OECD国家研究格局的定量分析及方法论考量[J]. 华东师范大学学报(教育科学版), 2025, 43(8): 30-50.

Marek Kwiek, Lukasz Szymula. Patterns of Scientific Attrition: A Quantitative Analysis of Research Landscapes in OECD Countries with Methodological Considerations[J]. Journal of East China Normal University(Educationa, 2025, 43(8): 30-50.

图/表 9

图 1

图 2

表 1

按性别分列的 2000 年科学家群体的Kaplan-Meier估计值（所有学科合计）"

时间（年）	女性（2000 年队列）			男性（2000 年队列）			总计（2000 年队列）
时间（年）	N	N （离开科学界）	KM 概率（停留）及 95% CI 和 SE	N	N （离开科学界）	KM 概率（停留）及 95% CI 和 SE	N	N （离开科学界）	KM 概率（停留）及 95% CI 和 SE
1	52,115	2,530	0.951 (0.950-0.953)¹	90,661	4,151	0.954 (0.953-0.956)¹	142,776	6,681	0.953 (0.951-0.954)¹
2	49,585	3,985	0.875 (0.872-0.878)¹	86,510	6,302	0.885 (0.883-0.887)¹	136,095	10,287	0.880 (0.877-0.882)¹
3	45,600	3,948	0.799 (0.796-0.803)²	80,208	6,114	0.817 (0.815-0.820)¹	125,808	10,062	0.811 (0.809-0.812)¹
4	41,652	3,553	0.731 (0.727-0.735)²	74,094	5,062	0.761 (0.759-0.764)¹	115,746	8,615	0.749 (0.746-0.751)¹
5	38,099	2,838	0.677 (0.673-0.681)²	69,032	4,356	0.713 (0.710-0.716)²	107,131	7,194	0.695 (0.691-0.698)²
6	35,261	2,602	0.627 (0.623-0.631)²	64,676	3,934	0.670 (0.667-0.673)²	99,937	6,536	0.655 (0.651-0.658)²
7	32,659	2,183	0.585 (0.581-0.589)²	60,742	3,458	0.632 (0.629-0.635)²	93,401	5,641	0.613 (0.610-0.616)²
8	30,476	1,961	0.547 (0.543-0.551)²	57,284	3,110	0.598 (0.594-0.601)²	87,760	5,071	0.577 (0.574-0.580)²
9	28,515	1,665	0.515 (0.511-0.520)²	54,174	2,774	0.567 (0.564-0.570)²	82,689	4,439	0.548 (0.545-0.551)²
10	26,850	1,472	0.487 (0.483-0.491)²	51,400	2,465	0.540 (0.537-0.543)²	78,250	3,937	0.517 (0.514-0.520)²
11	25,378	1,264	0.463 (0.458-0.467)²	48,935	2,225	0.515 (0.512-0.518)²	74,313	3,489	0.492 (0.489-0.495)²
12	24,114	1,158	0.440 (0.436-0.445)²	46,710	2,055	0.493 (0.489-0.496)²	70,824	3,213	0.466 (0.463-0.469)²
13	22,956	1,151	0.418 (0.414-0.423)²	44,655	2,032	0.470 (0.467-0.473)²	67,611	3,183	0.444 (0.441-0.447)²
14	21,805	1,089	0.398 (0.393-0.402)²	42,623	1,889	0.449 (0.446-0.453)²	64,428	2,978	0.426 (0.423-0.429)²
15	20,716	1,048	0.377 (0.373-0.382)²	40,734	1,884	0.429 (0.425-0.432)²	61,450	2,932	0.405 (0.402-0.408)²
16	19,668	1,033	0.358 (0.353-0.362)²	38,850	1,959	0.407 (0.404-0.410)²	58,518	2,992	0.384 (0.381-0.387)²
17	18,635	1,002	0.338 (0.334-0.342)²	36,891	2,020	0.385 (0.381-0.388)²	55,526	3,022	0.363 (0.360-0.366)²
18	17,633	1,064	0.318 (0.314-0.322)²	34,871	2,070	0.362 (0.359-0.365)²	52,504	3,134	0.342 (0.339-0.345)²
19	16,569	1,228	0.294 (0.290-0.298)²	32,801	2,350	0.336 (0.333-0.339)²	49,370	3,578	0.315 (0.312-0.318)²

表 1

图 3

表 2

图 4

图 5

图 6

图 7

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时间（年）	女性（2010 年队列）			男性（2010 年队列）
时间（年）	N	N（离开科学界）	KM 概率（停留）及 95% CI 和 SE	N	N（离开科学界）	KM 概率（停留）及 95% CI 和 SE
1	97,145	5,030	0.948 (0.947-0.950)¹	135,698	7,375	0.946 (0.944-0.947)¹
2	92,115	8,686	0.859 (0.857-0.861)¹	128,323	12,183	0.856 (0.854-0.858)¹
3	83,429	8,090	0.776 (0.773-0.778)¹	116,140	11,164	0.774 (0.771-0.776)¹
4	75,339	7,369	0.700 (0.697-0.703)¹	104,976	9,869	0.701 (0.698-0.703)¹
5	67,970	6,470	0.633 (0.630-0.636)²	95,107	8,552	0.638 (0.635-0.640)¹
6	61,500	5,904	0.572 (0.569-0.575)²	86,555	7,851	0.580 (0.577-0.583)¹
7	55,596	5,499	0.516 (0.513-0.519)²	78,704	7,322	0.526 (0.523-0.529)¹
8	50,097	4,984	0.464 (0.461-0.468)²	71,382	7,078	0.474 (0.471-0.477)¹
9	45,113	4,929	0.414 (0.411-0.417)²	64,304	6,745	0.424 (0.422-0.427)¹

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