Attribution Of Credit Research Articles

Importance of Name Disambiguation in Scientific Databases

Ambiguity in digital citation databases is a major bottleneck in attribution of proper credit to authors and thus hampers the process of profiling authors in true sense. It is quite common for academics and researchers to share common or similar names and the recent surge of digital citation records has amplified the problem exponentially. Realizing the prowess of information and communication technologies and the ease with which the information can be stored, managed and shared online, traditional publishers and databases have joined the bandwagon and embarked on the journey of digitizing their records. In the absence of an effective mechanism, it becomes extremely difficult for a computer to discriminate between similar entities and more so in case of our names. This paper highlights some of the major advantages and drawbacks of prominent categories of solutions by supporting the inferences with relevant backups, wherever required.

Credit attribution and collaborative work

We examine incentives in research teams where the market, such as the scientific community, attributes credit for success based on its inference of individual efforts. A social planner who could commit to credit ex ante would induce more effort from higher ability agents in exchange for less credit per unit effort. Lacking such commitment, the Bayesian market assigns credit proportional to perceived effort. This inability to distort credit per unit effort leads to an incentive reversal across projects. For “easy” projects with a concave marginal cost of effort, in the unique interior equilibrium, higher ability agents work less and receive lower credit/utility, while the opposite holds for “difficult” projects with a sufficiently convex marginal cost of effort. Moreover, equilibrium may involve over-investment by some team members who expect to receive most of the credit. The incentives to team up and the implications of effort observability on credit attribution are also investigated.

Stage 1 Registered Report: How responsibility attributions to self and others relate to outcome ownership in group decisions.

Responsibility judgements have important consequences in human society. Previous research focused on how someone's responsibility determines the outcome they deserve, for example, whether they are rewarded or punished. Here, we investigate the opposite link: How outcome ownership influences responsibility attributions in a social context. Participants in a group of three perform a majority vote decision-making task between gambles that can lead to a reward or no reward. Only one group member receives the outcome and participants evaluate their and the other players' responsibility for the obtained outcome. Two hypotheses are tested: 1) Whether outcome ownership increases responsibility attributions even when the control over an outcome is similar. 2) Whether people's tendency to attribute higher responsibility for positive vs negative outcomes will be stronger for players who received the outcome. The findings of this study may help reveal how credit attributions can be biased toward particular individuals who receive outcomes as a result of collective work.

Stage 1 Registered Report: How responsibility attributions to self and others relate to outcome ownership in group decisions.

Responsibility judgements have important consequences in human society. Previous research focused on how someone's responsibility determines the outcome they deserve, for example, whether they are rewarded or punished. Here, we investigate the opposite link: How outcome ownership influences responsibility attributions in a social context. Participants in a group of three perform a majority vote decision-making task between gambles that can lead to a reward or no reward. Only one group member receives the outcome and participants evaluate their and the other players' responsibility for the obtained outcome. Two hypotheses are tested: 1) Whether outcome ownership increases responsibility attributions even when the control over an outcome is similar. 2) Whether people's tendency to attribute higher responsibility for positive vs negative outcomes will be stronger for players who received the outcome. The findings of this study may help reveal how credit attributions can be biased toward particular individuals who receive outcomes as a result of collective work.

Effect of Robot Tutor’s Feedback Valence and Attributional Style on Learners

This study investigated the effect of a robot tutor’s feedback valence (positive feedback and negative feedback) and attributional style (internal-attribution and external-attribution) on learners. A Nao robot was designed to be a dance tutor that taught participants dance movements and gave performance feedback. Thirty-six graduate and undergraduate students participated in the experiments and were divided into two groups: an internal-attribution group and an external-attribution group. Participants in each group received both positive and negative feedback. The results indicated that the participants were more likely to accept positive feedback than negative feedback. In addition, they perceived greater influence by positive feedback than negative feedback. Moreover, participants did not the demonstrate self-serving attributional bias. They attributed more blame to themselves than to the robot for negative performance, whereas no difference was observed in the attribution of credit between the robot and the participants. Furthermore, the internal-attribution robot tutor was more likely to be regarded as a training coach instead of a training tool than the external-attribution robot tutor. Finally, participants reported a higher relationship closeness with the internal-attribution robot tutor than the external-attribution robot tutor.

Shadows and shields: Stars limit their collaborators’ exposure to attributions of both credit and blame

AbstractBuilding on the notion of cumulative advantage, we undertake a nuanced examination of how collaborating with a star affects attributions of credit and blame to nonstars in collaborative endeavors. Situating our inquiry in the US hedge fund industry, we hypothesize two‐way interactions predicting that collaboration with a star comanager will weaken both the positive effect of comanaged fund success and the negative effect of comanaged fund failure on nonstar managers’ professional status attainment (i.e., the status of a manager's subsequent employing firm). Specifically, we argue that the involvement of a star comanager will weaken prospective employers’ attributions for positive or negative performance to a focal nonstar manager, due to presumptions of the star's disproportionate influence in collaborative decisions. We then theorize a series of three‐way interactions specifying the roles of other signals of a nonstar manager's competence in this process. More precisely, we argue that a nonstar's performance outside the collaborative context and the status of the nonstar's current employer will weaken the dampening effect of comanaging with a star in the context of success and strengthen the favorable, blame‐reducing effect of comanaging with a star in the context of failure. Therefore, we suggest that nonstars who can signal their competence with these independent status signals will achieve greater professional status attainment than will those lacking such signals following both collaborative success and collaborative failure with a star. Our primary analyses support our hypotheses, while our supplementary analyses offer corroborative support for theorized mechanisms and evidence to address alternative explanations.

A science impact framework to measure impact beyond journal metrics.

Measuring the impact of public health science or research is important especially when it comes to health outcomes. Achieving the desired health outcomes take time and may be influenced by several contributors, making attribution of credit to any one entity or effort problematic. Here we offer a science impact framework (SIF) for tracing and linking public health science to events and/or actions with recognized impact beyond journal metrics. The SIF was modeled on the Institute of Medicine's (IOM) Degrees of Impact Thermometer, but differs in that SIF is not incremental, not chronological, and has expanded scope. The SIF recognizes five domains of influence: disseminating science, creating awareness, catalyzing action, effecting change and shaping the future (scope differs from IOM). For public health, the goal is to achieve one or more specific health outcomes. What is unique about this framework is that the focus is not just on the projected impact or outcome but rather the effects that are occurring in real time with the recognition that the measurement field is complex, and it takes time for the ultimate outcome to occur. The SIF is flexible and can be tailored to measure the impact of any scientific effort: from complex initiatives to individual publications. The SIF may be used to measure impact prospectively of an ongoing or new body of work (e.g., research, guidelines and recommendations, or technology) and retrospectively of completed and disseminated work, through linking of events using indicators that are known and have been used for measuring impact. Additionally, linking events offers an approach to both tell our story and also acknowledge other players in the chain of events. The value added by science can easily be relayed to the scientific community, policy makers and the public.

Gender Differences in Recognition for Group Work

We study whether gender influences credit attribution for group work using observational data and two experiments. We use data from academic economists to test whether coauthorship matters differen...

A qualitative study of Equal Co-First Authorship

ABSTRACT Over the past several years, there has been a significant increase in the number of scientific articles with two or more authors claiming “Equal Co-First Authorship” (ECFA). This study provides a critical background to ECFA designations, discusses likely causes of its increased use, and explores arguments for and against the practice. Subsequently, it presents the results of a qualitative study that sought the opinion of 19 authors listed among equal first authors of recent publications in leading scientific journals about ECFA designations. Results show that circumstances leading to ECFA designations vary significantly from each other. While the development of policies for these situations would not be easy, participants suggested that the lack of clear and consistent policies regarding the attribution and evaluation of ECFA contributes to tensions amongst ECFA authors and obscures their preferred attributions of credit.

Potential indicators of virus transport and removal during soil aquifer treatment of treated wastewater effluent

Increased water demands have led to a notable interest in the use of treated wastewater for reuse. Typically, this results from the implementation of advanced treatment of final effluent from wastewater treatment plants prior to reuse for potable or non-potable purposes.Soil aquifer treatment (SAT) is a natural treatment process in which water from sources of varying quality is infiltrated into the soil to further improve its quality. The goal of this study was to determine the log10 reduction values (LRVs) of viruses naturally present in treated effluent and evaluate two potential indicators of virus removal and transport, pepper mild mottle virus (PMMoV) and crAssphage, during SAT of treated effluent. Groundwater was sampled at three wells with different attributes within the Sweetwater Recharge Facility (SWRF) in Tucson, AZ. These sites vary greatly in operational parameters such as effluent infiltration rates and wetting/drying cycles, which may influence virus removal efficiency. Detection of adenovirus, enterovirus, PMMoV, and crAssphage were determined by qPCR/RT-qPCR and log10 reduction values (LRVs) were determined. PMMoV and crAssphage were detected in groundwater associated with a set of recharge basins that exhibited shorter wetting/drying cycles and faster infiltration rates. LRVs for crAssphage and PMMoV at this site ranged from 3.9 to 5.8, respectively. Moreover, PMMoV was detected downflow of the SAT sites, indicating the potential degradation of microbial groundwater quality in the region surrounding managed aquifer recharge facilities. Overall, PMMoV and crAssphage showed potential as conservative process indicators of virus removal during SAT, particularly for attribution of LRV credits. Moreover, the detection of these viruses indicated the potential influence of wetting/drying cycles on virus removal by SAT, a parameter that has not yet been studied with respect to biological contaminants.

Should I Blame the Human or the Robot? Attribution Within a Human–Robot Group

The present research studies the attribution of credit for success and attribution of blame for failure in a human–robot group. In the experiment, two participants of the same gender and a robot composed of a group that cooperated to solve the desert survival problem. Each group completed two tasks whose outcomes were manipulated: one task succeeded, and the other task failed. The participants were asked to attribute the credit of success and blame of failure to the group members, including the robot member, the human member, and the participants themselves. The results are as follows. First, participants attributed more credit and less blame to the robot member than to themselves. Second, participants attributed less blame to the human member than to themselves, and no significant difference was observed in the attribution of credit between them. Third, the robot member was more blamed than the human member, whereas they received similar levels of credit. Finally, compared with male participants, female participants assigned more credit and less blame to the robot member. The findings in this paper shed some light on the allocation of responsibility based on outcomes in human–robot groups.

CAWA: An Attention-Network for Credit Attribution

Credit attribution is the task of associating individual parts in a document with their most appropriate class labels. It is an important task with applications to information retrieval and text summarization. When labeled training data is available, traditional approaches for sequence tagging can be used for credit attribution. However, generating such labeled datasets is expensive and time-consuming. In this paper, we present Credit Attribution With Attention (CAWA), a neural-network-based approach, that instead of using sentence-level labeled data, uses the set of class labels that are associated with an entire document as a source of distant-supervision. CAWA combines an attention mechanism with a multilabel classifier into an end-to-end learning framework to perform credit attribution. CAWA labels the individual sentences from the input document using the resultant attention-weights. CAWA improves upon the state-of-the-art credit attribution approach by not constraining a sentence to belong to just one class, but modeling each sentence as a distribution over all classes, leading to better modeling of semantically-similar classes. Experiments on the credit attribution task on a variety of datasets show that the sentence class labels generated by CAWA outperform the competing approaches. Additionally, on the multilabel text classification task, CAWA performs better than the competing credit attribution approaches1.

Does the use of hedge derivatives improve the credit ratings of Brazilian companies?

ABSTRACT The purpose of this study is to identify the factors that may explain the attribution of credit ratings to firms, focusing especially on the impact of derivatives. The gap explored by this research lies in the novelty of analyzing how rating agencies perceive the effects caused by information related to derivatives use by Brazilian publicly-traded companies. In addition, this study shifts the previous findings from stock analysts to rating agencies, reinforcing the discussion about the complexity of derivatives in the credit risk assessment process. This research topic is currently of interest due to the adoption of International Financial Reporting Standard (IFRS) 9 (Accounting Pronouncements Committee - CPC - 48), which came into effect in January of 2018. Based on these rules, the main novelty presented in this article was its verification of the effect of the derivatives used by companies in order to hedge their credit ratings, thus helping to fill the empirical gap that exists in the literature from the area. The results found challenge the theory that the use of hedge derivatives is viewed positively by investors. However, although no significant statistical impact was found on the ratings of companies that use derivatives, it was observed that the companies that use derivatives and have the highest notional values were those that received the best ratings from Moody’s. With this we broadened the debate about the complexity of the information linked to derivatives use. In the study, 2,090 ratings attributed to non-financial companies with stocks traded on the Brasil, Bolsa, Balcão [B]³ exchange were examined between 2010 and 2016 by using panel data analysis, which lends robustness to the analysis and findings. Contrary to the central hypothesis of this research, the results presented here show that, in Brazil, companies that use derivative financial instruments for hedging do not receive the best credit ratings from rating agencies. One of the main contributions of this study is the evidence that Standard & Poor’s and Moody’s were unable to consistently incorporate information related to derivatives use, thus broadening the discussion about the complexity of these financial instruments.

Who are the acknowledgees? An analysis of gender and academic status

Acknowledgements found in scholarly papers allow for credit attribution of nonauthor contributors. As such, they are associated with a different kind of recognition than authorship. While several studies have shown that social factors affect authorship and citation practices,few analyses have been performed on acknowledgements. Based on 878,250 acknowledgees mentioned in 291,167 papers published between 2015 and 2017, this study analyzes the gender and academic status of individuals named in the acknowledgements of scientific papers. Our results show that gender disparities generally found in authorship can be extended to acknowledgements, and that women are even more underrepresented in acknowledgements section than in authors’ lists. Our findings also show that women acknowledge proportionally more women than men do. Regarding academic status, our results show that acknowledgees who have already published tend to have a higher position in the academic hierarchy compared with all Web of Science (WoS) authors. Taken together, these findings suggest that acknowledgement practices might be associated with academic status and gender.

To Credit or to Blame? The Asymmetric Impact of Government Responsibility in Economic News

Abstract This article studies the asymmetric effects of credit and blame attributions in economic news on government evaluations. We rely on a dataset combining a manual content analysis of Dutch economic news (print, television, online; N = 5,630) with a three-wave panel survey (N = 3,240) that was fielded in 2015. Results show that people who are exposed to news in which the government is blamed for the economy tend to adopt this frame by assigning responsibility to the government for the economic crisis. In addition, exposure to blame attributions leads to more negative government evaluations. This effect is partly mediated through the attribution of crisis responsibility. Credit attributions in the news do not have any effect on public opinion.

A review of author name disambiguation techniques for the PubMed bibliographic database

Author names in bibliographic databases often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associating a scholarly work with the person who wrote it, thereby introducing inaccuracy in credit attribution, bibliometric analysis, search-by-author in a digital library and expert discovery. A plethora of techniques for disambiguation of author names has been proposed in the literature. In this article, we focus on the research efforts targeted to disambiguate author names specifically in the PubMed bibliographic database. We believe this concentrated review will be useful to the research community because it discusses techniques applied to a very large real database that is actively used worldwide. We make a comprehensive survey of the existing author name disambiguation (AND) approaches that have been applied to the PubMed database: we organise the approaches into a taxonomy; describe the major characteristics of each approach including its performance, strengths, and limitations; and perform a comparative analysis of them. We also identify the datasets from PubMed that are publicly available for researchers to evaluate AND algorithms. Finally, we outline a few directions for future work.

Credit Attribution and Collaborative Work

We examine a dynamic model of teamwork in which the public attributes credit for success based on its perception of individual efforts. The collaborative behavior varies starkly depending on the shape of marginal effort cost, or project’s “difficulty.” In the unique (interior) equilibrium, higher ability collaborators work less and thus receive lower credit and payoff for “easy” projects, while the reverse holds for “difficult” projects. Despite free-riding, the team equilibrium may involve over-investment. Social efficiency requires over-rewarding collaborative work and under-rewarding solo work. The incentives to team up and the impact of effort monitoring on credit attribution are also investigated.

The History and Future of Data Citation in Practice

In this review, we adopt the definition that ‘Data citation is a reference to data for the purpose of credit attribution and facilitation of access to the data’ (TGDCSP 2013: CIDCR6). Furthermore, access should be enabled for both humans and machines (DCSG 2014). We use this to discuss how data citation has evolved over the last couple of decades and to highlight issues that need more research and attention. Data citation is not a new concept, but it has changed and evolved considerably since the beginning of the digital age. Basic practice is now established and slowly but increasingly being implemented. Nonetheless, critical issues remain. These issues are primarily because we try to address multiple human and computational concerns with a system originally designed in a non-digital world for more limited use cases. The community is beginning to challenge past assumptions, separate the multiple concerns (credit, access, reference, provenance, impact, etc.), and apply different approaches for different use cases.

Models and Surveillance Systems to Detect and Predict West Nile Virus Outbreaks

Over the past 20 yr, many models have been developed to predict risk for West Nile virus (WNV; Flaviviridae: Flavivirus) disease in the human population. These models have aided our understanding of the meteorological and land-use variables that drive spatial and temporal patterns of human disease risk. During the same period, electronic data systems have been adopted by surveillance programs across much of the United States, including a growing interest in integrated data services that preserve the autonomy and attribution of credit to originating agencies but facilitate data sharing, analysis, and visualization at local, state, and national scales. At present, nearly all predictive models have been limited to the scientific literature, with few having been implemented for use by public-health and vector-control decision makers. The current article considers the development of models for spatial patterns, early warning, and early detection of WNV over the last 20 yr and considers some possible paths toward increasing the utility of these models for guiding interventions.

Openness in Big Data and Data Repositories

There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.