BackgroundDeciphering the amount of work provided by different co-authors of a scientific paper has been a recurrent problem in science. Despite the myriad of metrics available, the scientific community still largely relies on the position in the list of authors to evaluate contributions, a metric that attributes subjective and unfounded credit to co-authors. We propose an easy to apply, universally comparable and fair metric to measure and report co-authors contribution in the scientific literature. MethodsThe proposed Author Contribution Index (ACI) is (...) based on contribution percentages provided by the authors, preferably at the time of submission. Researchers can use ACI to compare the contributions of different authors, describe the contribution profile of a particular researcher or analyse how contribution changes through time. We provide such an analysis based on contribution percentages provided by 97 scientists from the field of ecology who voluntarily responded to an online anonymous survey.ResultsACI is simple to understand and to implement because it is based solely on percentage contributions and the number of co-authors. It provides a continuous score that reflects the contribution of one author as compared to the average contribution of all other authors. For example, ACI(i) = 3, means that author i contributed three times more than what the other authors contributed on average. Our analysis comprised 836 papers published in 2014-2016 and revealed patterns of ACI values that relate to career advancement.ConclusionThere are many examples of author contribution indices that have been proposed but none has really been adopted by scientific journals. Many of the proposed solutions are either too complicated, not accurate enough or not comparable across articles, authors and disciplines. The author contribution index presented here addresses these three major issues and has the potential to contribute to more transparency in the science literature. If adopted by scientific journals, it could provide job seekers, recruiters and evaluating bodies with a tool to gather information that is essential to them and cannot be easily and accurately obtained otherwise. We also suggest that scientists use the index regardless of whether it is implemented by journals or not. (shrink)

With regard to current debates on the ethical implementation of AI, especially two demands are linked: the call for explainability and for ultimate human responsibility. In the medical field, both are condensed into the role of one person: It is the physician to whom AI output should be explainable and who should thus bear ultimate responsibility for diagnostic or treatment decisions that are based on such AI output. In this article, we argue that a black box AI indeed creates a (...) rationally irresolvable epistemic situation for the physician involved. Specifically, strange errors that are occasionally made by AI sometimes detach its output from human reasoning. Within this article it is further argued that such an epistemic situation is problematic in the context of ultimate human responsibility. Since said strange errors limit the promises of explainability and the concept of explainability frequently appears irrelevant or insignificant when applied to a diverse set of medical applications, we deem it worthwhile to reconsider the call for ultimate human responsibility. (shrink)

Individual choices are often inconsistent with economic theories, which has motivated a variety of ways to measure how far choices are from a given theory. Recent work has investigated the correlation between “measures of rationality” and observable information such as education or income. This paper investigates the sensitivity of this analysis to the units used to measure rationality, in particular we examine measures in percentage expenditure vs dollars expenditure. We find that correlations can change sign when we change the (...) units, but this sensitivity might be mitigated with experimental methods. (shrink)

A central feature of the Covid-19 pandemic is state differences. Some state Governors closed all but essential businesses, others did not. In some states, most of the population wore face coverings when in public; in other states, <50% wore face coverings. According to journalists, these differences were symptomatic of a politically polarized America. The Big 5 personality factors also cluster at the state level. For example, residents of Utah score high on Conscientiousness and low on Neuroticism, whereas residents of Massachusetts (...) and Connecticut show the opposite pattern. In state-level regressions that controlled for partisan political allegiances, Conscientiousness was a significant predictor of the stringency of state Covid-19 restrictions, whereas Openness was a significant predictor of mask wearing. A number of the predictors were strongly correlated with each other. For example, the correlation coefficient linking Openness with the percentage of Democratic state legislators was r = 0.53. Commonality regression partitions the explained variance between the amount that is unique to each predictor and the amount that is shared among subsets of correlated predictors. This approach revealed that the common variance shared by Conscientiousness, Openness and partisan politics accounted for 34% of the state differences in Covid-19 policy and 35% of the state differences in mask wearing. The results reflect the importance of personality in how Americans have responded to the Covid-19 pandemic. (shrink)

Work injury compensation and pensions are often determined according to medical disability rating scales attributing a percentage to each impaired body part or function. Incorporated into central medical–administrative networks of committees and examinations, these produce disability as a calculable space. This article examines the specific case of the Israeli National Insurance regulations regarding work injuries of 1956 and analyzes the shifted order they set. Looking at this system in the specific historical context of transition from the British Mandate workmen’s (...) compensation system to the “disability percentages” system, provides insight into the process of structuring and stabilizing durable numerical forms and the production of centralized government. Drawing on conceptualizations regarding the sociotechnical role of classification systems, I contend that through encoding and calculating classifications of disability, heterogeneity is eliminated and an ordering based on physiology is “black boxed,” illustrating the importance of technoscientific formations in structuring the “medical model of disability.”. (shrink)

A similarity index allowing comparisons of human populations has been defined as the common “Percentage of Isoactive Genes” or PIG, which can be calculated from any gene frequency distribution characterizing two populations. The complement to one of this value has been proved to be a distance, a measure which can be used in most techniques of cluster analysis as well as in usual representations of multivariated data (dendrograms, etc...). Furthermore, the formula can be generalized to a set of populations. (...) From a biological point of vue, PIG values are particularly meaningful, whereas other previously defined indices which tend to measure similarity or difference between populations neither have such an advantage, nor can they be expressed by a single and clear number like a percentage. Moreover,comparisons using PIG indices depend at first on the fluctuations of the most frequent genes of a distribution; on the other hand, different measures principally reflect the variations of low frequency genes, which unfortunately are nearly always poorly estimated, due to weak samples of populations.Interestingly, PIG values can be applied to any frequency distribution of any kind of objects. They can also extend to a whole set of distributions by using a mean value. When applying these measures to a series of polymorphic genetical systems such as ABO, Rhesus, MNSs, Gm and HLA, one can account for the variability or the homogeneity particular to the different human groups, with simple and objective criteria. (shrink)