Dataset Information

Collective credit allocation in science.

ABSTRACT: Collaboration among researchers is an essential component of the modern scientific enterprise, playing a particularly important role in multidisciplinary research. However, we continue to wrestle with allocating credit to the coauthors of publications with multiple authors, because the relative contribution of each author is difficult to determine. At the same time, the scientific community runs an informal field-dependent credit allocation process that assigns credit in a collective fashion to each work. Here we develop a credit allocation algorithm that captures the coauthors' contribution to a publication as perceived by the scientific community, reproducing the informal collective credit allocation of science. We validate the method by identifying the authors of Nobel-winning papers that are credited for the discovery, independent of their positions in the author list. The method can also compare the relative impact of researchers working in the same field, even if they did not publish together. The ability to accurately measure the relative credit of researchers could affect many aspects of credit allocation in science, potentially impacting hiring, funding, and promotion decisions.

SUBMITTER: Shen HW

PROVIDER: S-EPMC4151753 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Publications

Collective credit allocation in science.

Shen Hua-Wei HW Barabási Albert-László AL

Proceedings of the National Academy of Sciences of the United States of America 20140811 34

Collaboration among researchers is an essential component of the modern scientific enterprise, playing a particularly important role in multidisciplinary research. However, we continue to wrestle with allocating credit to the coauthors of publications with multiple authors, because the relative contribution of each author is difficult to determine. At the same time, the scientific community runs an informal field-dependent credit allocation process that assigns credit in a collective fashion to ...[more]

PMID: 25114238

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Project description:BackgroundCommunity-based interventions have shown promise in reducing childhood overweight and obesity. However, they have been critiqued for using linear logic models. Participatory community-based systems approaches are posited as addressing the complexity of non-linear relationships in a local context. Community members are empowered to understand and describe obesity causation, identify and prioritise possible solutions. The application of such approaches to childhood obesity is in its infancy.AimTo describe the first 12 months of a participatory whole-of-community systems approach to creating collective action to tackle childhood obesity, called GenR8 Change, in a local government area of Victoria, Australia.MethodsThree group model building (GMB) sessions focused on the development of a causal loop diagram (CLD), prioritised evidence-informed actions, and developed implementation strategies. The collective impact framework underpinned the approach, with a local backbone group supporting community members to implement prioritised actions.ResultsThe first two GMB sessions included 20 key community leaders where a CLD examining the factors contributing to childhood obesity in the community was constructed and refined (22 variables GMB1, 53 variables GMB2). In the third session, 171 members of the wider community further refined the CLD, identified priorities for childhood obesity prevention (72 variables in final CLD). One-hundred and thirteen individuals signed up across 13 working groups to plan and implement 53 prioritised actions. Agreed community actions included creating sugar free zones; developing healthy policies; increasing breastfeeding rates; improving drinking water access; and increasing physical activity options. Twelve months post-GMB3, 115 actions had been implemented.ConclusionGenR8 Change is one of the first communities to apply systems thinking to childhood obesity prevention. Knowledge on how to collectively identify relevant leverage points to tackle childhood obesity can now be shared with other communities.

Project description:BackgroundLong-term mental health (MH) policies in Finland aimed at investing in community care and promoting reforms have led to a reduction in the number of psychiatric hospital beds. However, most resources are still allocated to hospital and community residential services due to various social, economic and political factors. Despite previous research focussing on the number and cost of these services, no study has evaluated the emerging patterns of use, their technical performance and the relationship with the workforce structure.ObjectiveThe purpose of this study was to observe the patterns of use and their technical performance (efficiency) of the main types of care of MH services in the Helsinki-Uusimaa region (Finland), and to analyse the potential relationship between technical performance and the corresponding workforce structure.MethodsThe sample included acute hospital residential care, non-hospital residential care and outpatient care services. The analysis was conducted using regression analysis, Monte Carlo simulation, fuzzy inference and data envelopment analysis.ResultsThe analysis showed a statistically significant linear relationship between the number of service users and the length of stay, number of beds in non-hospital residential care and number of contacts in outpatient care services. The three service types displayed a similar pattern of technical performance, with high relative technical efficiency on average and a low probability of being efficient. The most efficient acute hospital and outpatient care services integrated multidisciplinary teams, while psychiatrists and nurses characterized non-hospital residential care.ConclusionsThe results indicated that the number of resources and utilization variables were linearly related to the number of users and that the relative technical efficiency of the services was similar across all types. This suggests homogenous MH management with small variations based on workforce allocation. Therefore, the distribution of workforce capacity should be considered in the development of effective policies and interventions in the southern Finnish MH system.

Dataset Information

Collective credit allocation in science.

Publications

Collective credit allocation in science.

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