The impact of interdisciplinarity on public engagement in citizen science

Abstract

Abstract:

Interdisciplinary approaches are widely advocated in global science policy as a key strategy for addressing complex social issues. However, evidence linking interdisciplinary research to its impact on the public remains limited. It is generally believed that interdisciplinary research produces solutions to critical problems by recombining knowledge from multiple fields. Yet the public often struggles to appreciate this recombination, since most individuals are not as specialized as patent examiners. To address this gap, our study introduces a more accessible attribute of interdisciplinary scientific knowledge—that of being a public good—alongside its re-combinatory nature, to explain how interdisciplinary citizen science fosters public engagement. Drawing on classic signaling theory from product management research, as well as social cognition theory and science communication theories, we focus on the signaling system formed by citizen science project teams, the public, and society as a whole. Based on this framework, we develop a theoretical model to examine how interdisciplinarity influences the public’s willingness to participate in citizen science projects. According to social role theory, we also consider how a research team’s identity might moderate the effect of interdisciplinarity on engagement willingness. Our study analyzes 880 citizen science projects hosted on Experiment (experiment.com) and engagement data from 37,867 contributors. We employ text‐mining methods—including Latent Dirichlet Allocation topic modeling and Word2Vec—to quantify three dimensions of interdisciplinarity in project titles and descriptions: diversity, balance, and disparity. Validation through manual checks and algorithmic tests confirms the robustness of our quantification approach, demonstrating that text mining effectively identifies disciplinary topics. Empirical results show that interdisciplinarity in citizen science projects promotes public engagement through two mechanisms. First, there is a direct positive effect: projects spanning diverse disciplines attract both broader and deeper public engagement. Second, there is an indirect positive effect: interdisciplinarity reduces a project’s novelty, thereby mitigating novelty’s negative impact on public engagement willingness; this indirect effect, however, pertains only to public engagement breadth. We find that when the research team is affiliated with academia, the direct positive effect of interdisciplinarity on public engagement breadth is strengthened, although no significant moderation appears for public engagement depth. Furthermore, when disciplinary diversity or balance is high, teams composed of both academic and amateur members negatively moderate interdisciplinarity’s impact on public engagement breadth. Under conditions of high disciplinary balance, such mixed teams also negatively moderate the effect on public engagement depth. In contrast, amateur teams’ identity shows no significant moderating effects. Using the Web of Science classification system and project tags, we classify citizen science projects as either natural science-oriented or humanities and social science-oriented. In natural science-oriented projects, interdisciplinarity enhances both public engagement breadth and depth, and the moderating effects of team identity align with our main findings. However, in humanities and social science-oriented projects, interdisciplinarity negatively affects public engagement breadth, though academic team can offset or even reverse this negative effect. This study enriches understanding of how interdisciplinary research influences public engagement. Practically, it offers guidance for government agencies and scientists in managing interdisciplinary citizen science activities.

Key words: citizen science, interdisciplinarity research, signaling theory, scientific knowledge, topic modeling

摘要：

科学政策呼吁将跨学科作为一种解决复杂社会问题的策略,但目前关于跨学科研究与其社会公众影响的证据仍然较少。本文关注科学知识的公共产品和重组两类属性,基于信号理论探讨了跨学科性对科学项目公众参与的影响。采用Experiment平台上的880个公众科学项目及其37,867人次公众参与的真实数据进行分析,结果表明,公众科学项目的跨学科性能够促进公众参与,包括直接影响和通过新颖性的中介产生的间接影响;学术研究团队身份能够正向调节跨学科性的直接影响,在特定跨学科策略下学术-非学术的混合团队身份会负向调节这种影响;在人文和社会科学导向和自然科学导向的公众科学项目中,跨学科性对公众参与的影响以及研究团队身份的调节作用存在显著差异。本文丰富了对跨学科研究产生社会影响的理解,为研究人员发起跨学科公众科学项目、开展跨学科公众科学实践提供了经验参考。

关键词: 公众科学, 跨学科研究, 信号理论, 科学知识, 主题模型

CLC Number:

G301

MIN Chao, WU Bing-yi, WANG Wei. The impact of interdisciplinarity on public engagement in citizen science[J]. Studies in Science of Science, 2026, 44(6): 1307-1319.

闵超, 吴炳毅, 王伟. 跨学科性对科学项目公众参与的影响[J]. 科学学研究, 2026, 44(6): 1307-1319.

Figures/Tables 11

References 26

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类型	变量	最小值	中位数	最大值	均值	标准差
因变量	Backers	0	29	537	43.031	52.971
因变量	Money	0	72.72	10000	243.986	658.638
自变量	Variety	1	1	4	1.248	0.467
	Balance	0.018	0.025	1.775	0.27	0.348
	Disparity	0	0	0.658	0.087	0.166
中介变量	Novelty	0.588	0.963	0.998	0.885	0.13
调节变量	Academia (1=True)	0	1	1	0.581	0.494
	Amateur (1=True)	0	0	1	0.141	0.348
	Mixed (1=True)	0	0	1	0.278	0.448
控制变量	Challenge (1=True)	0	0	1	0.475	0.5
	Institution (1=True)	0	1	1	0.824	0.381
	NumGoal	1	4000	150000	6095.5	12203.413
	NumBudget	1	5	29	5.275	3.187
	NumNotes	0	4	44	5.728	6.522
	NumDiscussion	0	5	188	10.511	18.074
	NumEndorse	0	2	19	2.085	2.03
	NumTeam	1	2	23	2.96	2.281
	LengthIntroduce	183	2705.5	3444	2634.66	268.782

类型	变量	最小值	中位数	最大值	均值	标准差
因变量	Backers	0	29	537	43.031	52.971
因变量	Money	0	72.72	10000	243.986	658.638
自变量	Variety	1	1	4	1.248	0.467
	Balance	0.018	0.025	1.775	0.27	0.348
	Disparity	0	0	0.658	0.087	0.166
中介变量	Novelty	0.588	0.963	0.998	0.885	0.13
调节变量	Academia (1=True)	0	1	1	0.581	0.494
	Amateur (1=True)	0	0	1	0.141	0.348
	Mixed (1=True)	0	0	1	0.278	0.448
控制变量	Challenge (1=True)	0	0	1	0.475	0.5
	Institution (1=True)	0	1	1	0.824	0.381
	NumGoal	1	4000	150000	6095.5	12203.413
	NumBudget	1	5	29	5.275	3.187
	NumNotes	0	4	44	5.728	6.522
	NumDiscussion	0	5	188	10.511	18.074
	NumEndorse	0	2	19	2.085	2.03
	NumTeam	1	2	23	2.96	2.281
	LengthIntroduce	183	2705.5	3444	2634.66	268.782

DV	Novelty
	Model 1	Model 2	Model 3
Year dummy	Yes	Yes	Yes
Variety	-0.020^** (0.009)
Balance		-0.042^*** (0.012)
Disparity			-0.068^*** (0.026)
Constant	1.221^*** (0.148)	1.233^*** (0.149)	1.206 ^*** (0.147)
F-statistic	3.52^***	3.90^***	3.64^***
Observations	880	880	880
Adjusted R²	0.054	0.062	0.057

DV	Novelty
	Model 1	Model 2	Model 3
Year dummy	Yes	Yes	Yes
Variety	-0.020^** (0.009)
Balance		-0.042^*** (0.012)
Disparity			-0.068^*** (0.026)
Constant	1.221^*** (0.148)	1.233^*** (0.149)	1.206 ^*** (0.147)
F-statistic	3.52^***	3.90^***	3.64^***
Observations	880	880	880
Adjusted R²	0.054	0.062	0.057

DV	IV	模型	ACME	ADE	TE	PM
Backers (In)	Variety	Model 1	0.008^*	0.111^**	0.119^***	0.069^*
	Balance	Model 2	0.017^**	0.118^*	0.135^**	0.125^*
	Disparity	Model 3	0.028^**	0.253^**	0.280^**	0.099^*
Money (In)	Variety	Model 4	-0.006	0.149^**	0.144^**	-0.038
	Balance	Model 5	-0.012	0.157	0.145	-0.083
	Disparity	Model 6	-0.019^*	0.421^**	0.401^**	-0.048