11,635 research outputs found
A quantitative perspective on ethics in large team science
The gradual crowding out of singleton and small team science by large team
endeavors is challenging key features of research culture. It is therefore
important for the future of scientific practice to reflect upon the individual
scientist's ethical responsibilities within teams. To facilitate this
reflection we show labor force trends in the US revealing a skewed growth in
academic ranks and increased levels of competition for promotion within the
system; we analyze teaming trends across disciplines and national borders
demonstrating why it is becoming difficult to distribute credit and to avoid
conflicts of interest; and we use more than a century of Nobel prize data to
show how science is outgrowing its old institutions of singleton awards. Of
particular concern within the large team environment is the weakening of the
mentor-mentee relation, which undermines the cultivation of virtue ethics
across scientific generations. These trends and emerging organizational
complexities call for a universal set of behavioral norms that transcend team
heterogeneity and hierarchy. To this end, our expository analysis provides a
survey of ethical issues in team settings to inform science ethics education
and science policy.Comment: 13 pages, 5 figures, 1 table. Keywords: team ethics; team management;
team evaluation; science of scienc
Modeling Collaboration in Academia: A Game Theoretic Approach
In this work, we aim to understand the mechanisms driving academic
collaboration. We begin by building a model for how researchers split their
effort between multiple papers, and how collaboration affects the number of
citations a paper receives, supported by observations from a large real-world
publication and citation dataset, which we call the h-Reinvestment model. Using
tools from the field of Game Theory, we study researchers' collaborative
behavior over time under this model, with the premise that each researcher
wants to maximize his or her academic success. We find analytically that there
is a strong incentive to collaborate rather than work in isolation, and that
studying collaborative behavior through a game-theoretic lens is a promising
approach to help us better understand the nature and dynamics of academic
collaboration.Comment: Presented at the 1st WWW Workshop on Big Scholarly Data (2014). 6
pages, 5 figure
Incentives and Efficiency in Uncertain Collaborative Environments
We consider collaborative systems where users make contributions across
multiple available projects and are rewarded for their contributions in
individual projects according to a local sharing of the value produced. This
serves as a model of online social computing systems such as online Q&A forums
and of credit sharing in scientific co-authorship settings. We show that the
maximum feasible produced value can be well approximated by simple local
sharing rules where users are approximately rewarded in proportion to their
marginal contributions and that this holds even under incomplete information
about the player's abilities and effort constraints. For natural instances we
show almost 95% optimality at equilibrium. When players incur a cost for their
effort, we identify a threshold phenomenon: the efficiency is a constant
fraction of the optimal when the cost is strictly convex and decreases with the
number of players if the cost is linear
Will This Paper Increase Your h-index? Scientific Impact Prediction
Scientific impact plays a central role in the evaluation of the output of
scholars, departments, and institutions. A widely used measure of scientific
impact is citations, with a growing body of literature focused on predicting
the number of citations obtained by any given publication. The effectiveness of
such predictions, however, is fundamentally limited by the power-law
distribution of citations, whereby publications with few citations are
extremely common and publications with many citations are relatively rare.
Given this limitation, in this work we instead address a related question asked
by many academic researchers in the course of writing a paper, namely: "Will
this paper increase my h-index?" Using a real academic dataset with over 1.7
million authors, 2 million papers, and 8 million citation relationships from
the premier online academic service ArnetMiner, we formalize a novel scientific
impact prediction problem to examine several factors that can drive a paper to
increase the primary author's h-index. We find that the researcher's authority
on the publication topic and the venue in which the paper is published are
crucial factors to the increase of the primary author's h-index, while the
topic popularity and the co-authors' h-indices are of surprisingly little
relevance. By leveraging relevant factors, we find a greater than 87.5%
potential predictability for whether a paper will contribute to an author's
h-index within five years. As a further experiment, we generate a
self-prediction for this paper, estimating that there is a 76% probability that
it will contribute to the h-index of the co-author with the highest current
h-index in five years. We conclude that our findings on the quantification of
scientific impact can help researchers to expand their influence and more
effectively leverage their position of "standing on the shoulders of giants."Comment: Proc. of the 8th ACM International Conference on Web Search and Data
Mining (WSDM'15
Decentring the discoverer: how AI helps us rethink scientific discovery
This paper investigates how intuitions about scientific discovery using artificial intelligence (AI) can be used to improve our understanding of scientific discovery more generally. Traditional accounts of discovery have been agent-centred: they place emphasis on identifying a specific agent who is responsible for conducting all, or at least the important part, of a discovery process. We argue that these accounts experience difficulties capturing scientific discovery involving AI and that similar issues arise for human discovery. We propose an alternative, collective-centred view as superior for understanding discovery, with and without AI. This view maintains that discovery is performed by a collective of agents and entities, each making contributions that differ in significance and character, and that attributing credit for discovery depends on various finer-grained properties of the contributions made. Detailing its conceptual resources, we argue that this view is considerably more compelling than its agent-centred alternative. Considering and responding to several theoretical and practical challenges, we point to concrete avenues for further developing the view we propose
Increasing innovative activity in the UK? Where now for government support for innovation and technology transfer?
In this Briefing Note, we present new evidence on the UK’s innovative performance and provide a summary of government business support programmes aimed at fostering innovative activity and technology transfer. Following recent reviews of policy in this area, there remain a number of such schemes in operation. We discuss the rationales for each, including the extent to which they overlap, and suggest some ways in which evidence on take-up and on effectiveness might be used to guide any future policy changes in this area
Connecting strategy and system dynamics: an example and lessons learned
This article is based on my talk at the 2015 International System Dynamics Conference upon receiving the Jay W. Forrester Award for the article “Impact of growth opportunities and competition on firm-level capability development tradeoffs” (Organization Science 2012; 23(1): 138–154). It summarizes how that research connects strategy concepts with system dynamics (SD) modeling to inform the pressures managers face to focus on the short term as a result of endogenous growth opportunities and competition. Drawing on this example I discuss some potentially useful research tools and assumptions. I close by sharing personal reflections on the process of writing for non-SD academics and why I think making those connections is worthwhile
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