•We discuss how Citizen Science (CS) can contribute to sustainability transitions.•Pathways include shaping research agendas; mobilizing resources; facilitating socio-technical co-evolution.•Several ...challenges arise for CS particularly in the context of sustainability transitions.•We substantiate our arguments using a wide range of case examples.•We discuss implications for future research, citizens and scientists, as well as policy makers.
Citizen Science (CS) projects involve members of the general public as active participants in research. While some advocates hope that CS can increase scientific knowledge production (“productivity view”), others emphasize that it may bridge a perceived gap between science and the broader society (“democratization view”). We discuss how an integration of both views can allow Citizen Science to support complex sustainability transitions in areas such as renewable energy, public health, or environmental conservation. We first identify three pathways through which such impacts can occur: (1) Problem identification and agenda setting; (2) Resource mobilization; and (3) Facilitating socio-technical co-evolution. To realize this potential, however, CS needs to address important challenges that emerge especially in the context of sustainability transitions: Increasing the diversity, level, and intensity of participation; addressing the social as well as technical nature of sustainability problems; and reducing tensions between CS and the traditional institution of academic science. Grounded in a review of academic literature and policy reports as well as a broad range of case examples, this article contributes to scholarship on science, innovation, and sustainability transitions. We also offer insights for actors involved in initiating or institutionalizing Citizen Science efforts, including project organizers, funding agencies, and policy makers.
Scientific research performed with the involvement of the broader public (the crowd) attracts increasing attention from scientists and policy makers. A key premise is that project organizers may be ...able to draw on underused human resources to advance research at relatively low cost. Despite a growing number of examples, systematic research on the effort contributions volunteers are willing to make to crowd science projects is lacking. Analyzing data on seven different projects, we quantify the financial value volunteers can bring by comparing their unpaid contributions with counterfactual costs in traditional or online labor markets. The volume of total contributions is substantial, although some projects are much more successful in attracting effort than others. Moreover, contributions received by projects are very uneven across time—a tendency toward declining activity is interrupted by spikes typically resulting from outreach efforts or media attention. Analyzing user-level data, we find that most contributors participate only once and with little effort, leaving a relatively small share of users who return responsible for most of the work. Although top contributor status is earned primarily through higher levels of effort, top contributors also tend to work faster. This speed advantage develops over multiple sessions, suggesting that it reflects learning rather than inherent differences in skills. Our findings inform recent discussions about potential benefits from crowd science, suggest that involving the crowd may be more effective for some kinds of projects than others, provide guidance for project managers, and raise important questions for future research.
Significance Involving the public in research may provide considerable benefits for the progress of science. However, the sustainability of “crowd science” approaches depends on the degree to which members of the public are interested and provide continued labor inputs. We describe and compare contribution patterns in multiple projects using a range of measures. We show that effort contributions can be significant in magnitude and speed, but we also identify several challenges. In addition, we explore some of the underlying dynamics and mechanisms. As such, we provide quantitative evidence that is useful for scientists who consider adopting crowd science approaches and for scholars studying crowd-based knowledge production. Our results also inform current policy discussions regarding the organization of scientific research.
The literature of pedestrian, crowd and evacuation dynamics is captured in its approximate full scope and is analysed at various levels using scientometric indicators of its underlying articles ...(N≈6200). The analyses provide new insight into the structural make-up of this field, its distribution across various disciplines, its temporal and historical patterns of development, as well as its pioneering and influential entities (i.e., articles and authors). The analysis establishes that the field has exerted a high degree of influence beyond its borders while identifying these areas of influence. Studies with greatest impact within and beyond the borders of crowd dynamics literature; as well as, pioneering but neglected studies of the field are identified. It is determined that the beginning of the twenty-first century marks the most important milestone of this field, an intellectual turning point that set the field to become a standalone research domain. Temporal analysis indicates certain paradigm shifts in crowed dynamics research during the past decade. It identifies two streams of activities labelled “empirical methods” and “crowd counting/visual crowd analysis” as the two youngest and currently hottest research streams of this field. Outcomes suggest that further interactions and collaborations between the computer vision and the mainstream of crowd researchers could be warranted. This could lead to the next generations of data-driven crowd models and prevent the field from going to a state of stagnation. It is also hoped that these outcomes contribute to enhancing the quality (i.e., specificity and inclusiveness) of document referencing in crowd dynamics papers.
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•Pedestrian dynamics literature is analysed in its full scale using bibliometric indicators.•A systematic search query is proposed that can be used as a standard for tracking the literature.•History of the field, current/past trends, influential entities & fundamental but neglected studies documented.•The division of human behaviour in fire is the pioneering division of the field.•Streams of “empirical methods” and “visual crowd analysis” are found to be currently trending.•The computer-vision sector is substantial in size and activity but rather isolated from mainstream.
•We introduce the reader to crowd science by discussing prominent projects including Foldit, Galaxy Zoo, and Polymath.•We identify key characteristics of crowd science projects and distinguish them ...from other modes of knowledge production.•We consider heterogeneity among crowd science projects and discuss which types of scientific problems may benefit most (and least) from crowd science.•We discuss organizational challenges crowd science projects face and conjecture how these challenges may be overcome.•We conclude with an agenda for future research as well as implications for funding agencies and policy makers.
A growing amount of scientific research is done in an open collaborative fashion, in projects sometimes referred to as “crowd science”, “citizen science”, or “networked science”. This paper seeks to gain a more systematic understanding of crowd science and to provide scholars with a conceptual framework and an agenda for future research. First, we briefly present three case examples that span different fields of science and illustrate the heterogeneity concerning what crowd science projects do and how they are organized. Second, we identify two fundamental elements that characterize crowd science projects – open participation and open sharing of intermediate inputs – and distinguish crowd science from other knowledge production regimes such as innovation contests or traditional “Mertonian” science. Third, we explore potential knowledge-related and motivational benefits that crowd science offers over alternative organizational modes, and potential challenges it is likely to face. Drawing on prior research on the organization of problem solving, we also consider for what kinds of tasks particular benefits or challenges are likely to be most pronounced. We conclude by outlining an agenda for future research and by discussing implications for funding agencies and policy makers.
Artificial intelligence (AI) can perform core research tasks such as generating research questions, processing data, and solving problems. We shift the focus from AI as a “worker” to ask whether, ...how, and when AI can also “manage” human workers who perform such tasks. Focusing on the context of crowd science, we find examples of algorithmic management (AM) in five key functions highlighted in prior organizational literature: task division and task allocation, direction, coordination, motivation, and supporting learning. These applications benefit from the instantaneous, comprehensive, and interactive capabilities of AI, and reflect several more general underlying functions such as matching, clustering, and forecasting. Quantitative comparisons show that projects using AM are larger and more likely to be associated with platforms than projects not using AM, pointing to potentially important contingency factors. We conclude by outlining an agenda for future research on algorithmic management in scientific research.
•We study whether, how, and when Artificial Intelligence (AI) can manage human workers in scientific research.•Our empirical context is Crowd Science: Projects that involve non-professional and professional scientists in collaborative research.•AI can perform five management functions: Task division/allocation, direction, coordination, motivation, supporting learning.•Large projects and platform-affiliated projects are more likely to adopt algorithmic management.•We outline an agenda for future research on algorithmic management in science.
With the development of modern science and economy, congestions and accidents are brought by increasing traffics. And to improve efficiency, traffic signal based control is usually used as an ...effective model to alleviate congestions and to reduce accidents. However, the fixed mode of existing phase and cycle time restrains the ability to satisfy ever complex environments, which lead to a low level of efficiency. To further improve traffic efficiency, this paper proposes a crowd-based control model to adapt complex traffic environments. In this model, subjects are deemed as digital selves who can perform actions in complex traffic environments, such as vehicles and traffic lights. These digital selves have their own control processing mechanisms, properties, and behaviors. And each digital self is continuously optimizing its behaviors according to its learning ability, road conditions, and information interactions from connections with the others. Without a fixed structure, the connections are diverse and random to form a more complex traffic environment, which may be connected or disappeared at any time with continues movements. Finally, feasibility and effectiveness of the crowd-based traffic control model is demonstrated by comparison with fixed traffic signal control model, indicating that the model can alleviate traffic congestion effectively.
•Crowd involvement in research tends to be limited to empirical activities.•We study the nature and quality of research questions (RQ) generated by crowd members.•Average crowd RQs have lower ...novelty, scientific impact but similar practical impact as professional RQs.•Crowd RQs outperform on all dimensions after selection within or across individuals.•We interpret the results in light of five crowd “paradigms” distilled from prior literature.
Scientists are increasingly crossing the boundaries of the professional system by involving the general public (the crowd) directly in their research. However, this crowd involvement tends to be confined to empirical work and it is not clear whether and how crowds can also be involved in conceptual stages such as formulating the questions that research is trying to address. Drawing on five different “paradigms” of crowdsourcing and related mechanisms, we first discuss potential merits of involving crowds in the formulation of research questions (RQs). We then analyze data from two crowdsourcing projects in the medical sciences to describe key features of RQs generated by crowd members and compare the quality of crowd contributions to that of RQs generated in the conventional scientific process. We find that the majority of crowd contributions are problem restatements that can be useful to assess problem importance but provide little guidance regarding potential causes or solutions. At the same time, crowd-generated research questions frequently cross disciplinary boundaries by combining elements from different fields within and especially outside medicine. Using evaluations by professional scientists, we find that the average crowd contribution has lower novelty and potential scientific impact than professional research questions, but comparable practical impact. Crowd contributions outperform professional RQs once we apply selection mechanisms at the level of individual contributors or across contributors. Our findings advance research on crowd and citizen science, crowdsourcing and distributed knowledge production, as well as the organization of science. We also inform ongoing policy debates around the involvement of citizens in research in general, and agenda setting in particular.
Reproducibility in Management Science Fišar, Miloš; Greiner, Ben; Huber, Christoph ...
Management science,
03/2024, Letnik:
70, Številka:
3
Journal Article
Recenzirano
Odprti dostop
With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal
Management Science
before and after the introduction of a new Data and Code ...Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies and suggest potential avenues for enhancing their effectiveness.
This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis–fast track.
Supplemental Material:
The online appendices and data are available at
https://doi.org/10.1287/mnsc.2023.03556
.
Research projects that actively involve 'crowds' or non-professional 'citizen scientists' are attracting growing attention. Such projects promise to increase scientific productivity while also ...connecting science with the general public. We make three contributions. First, we argue that the largely separate literatures on 'Crowd Science' and 'Citizen Science' investigate strongly overlapping sets of projects but take different disciplinary lenses. Closer integration can enrich research on Crowd and Citizen Science (CS). Second, we propose a framework to profile projects with respect to four types of crowd contributions: activities, knowledge, resources, and decisions. This framework also accommodates machines and algorithms, which increasingly complement or replace professional and non-professional researchers as a third actor. Finally, we outline a research agenda anchored on important underlying organisational challenges of CS projects. This agenda can advance our understanding of Crowd and Citizen Science, yield practical recommendations for project design, and contribute to the broader organisational literature.
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