Multiple inherent biases related to different citation practices (for e.g., self-citations, negative citations, wrong citations, multi-authorship-biased citations, honorary citations, circumstantial citations, discriminatory citations, selective and arbitrary citations, etc.) make citation-based bibliometrics strongly flawed and defective measures. A paper can be highly cited for a while (for e.g., under circumstantial or transitional knowledge), but years later it may appear that its findings, paradigms, or theories were untrue or invalid anymore. By contrast, a paper may remain shelved or overlooked for years or (...) decades, but new studies or discoveries may actualize its subject at any moment. As citation-based metrics are transformed into "commercial activities," the "citation credit" should be considered on a commercial basis too, in the sense that "citation credit" should be shared out as a "citation dividend" by shareholders (coauthors) averagely or proportionally to their contributions but not fully appropriated by each of them. At equal numbers of citations, the greater number of authors, the lower "citation credit" should be and vice versa. Overlooking the presence of distorted and subjective citation practices makes many people and administrators "obsessed" with the number of citations to such an extent to run after "highly cited" authors and to create specialized citation databases for commercial purposes. Citation-based bibliometrics, however, are unreliable and unscientific measures; citation counts do not mean that a more cited work is of a higher quality or accuracy than a less cited work because citations do not measure the quality or accuracy. Citations do not mean that a highly cited author or journal is more commendable than a less cited author or journal. Citations are not more than countable numbers: no more, no less. (shrink)

This study assesses the retractions of scientific articles in engineering journals indexed on the Web of Science from 1945 to 2015. The data set was built based on documents containing the keywords retracted, retraction, withdrawal, or redress. We used database exploration techniques, including Structured Query Language and analysis of variance, for data analysis. We analyzed 238 retractions published by 117 journals. The most common reason for retraction was unethical research, and higher impact factors journals tended to publish more retractions. In (...) conclusion, most of the analyzed retractions were the result of unethical research and were retracted by editors. (shrink)

Scientific publication is immensely important to the scientific endeavour. There is, however, concern that rewarding scientists chiefly on publication creates a perverse incentive, allowing careless and fraudulent conduct to thrive, compounded by the predisposition of top-tier journals towards novel, positive findings rather than investigations confirming null hypothesis. This potentially compounds a reproducibility crisis in several fields, and risks undermining science and public trust in scientific findings. To date, there has been comparatively little modelling on factors that influence science trustworthiness, despite (...) the importance of quantifying the problem. We present a simple phenomenological model with cohorts of diligent, careless and unethical scientists, with funding allocated by published outputs. This analysis suggests that trustworthiness of published science in a given field is influenced by false positive rate, and pressures for positive results. We find decreasing available funding has negative consequences for resulting trustworthiness, and examine strategies to combat propagation of irreproducible science. (shrink)

With the growth of research collaborations, the average number of authors per article and the phenomenon of equally important authorships have increased. The essence of the phenomenon of equally important authorships is the approximately equal importance of authors, both because of the difficulties in comparing authors’ contributions to a paper and some actual research evaluation practices, which give full paper credit only to the most important authors. A mechanism for indicating that various authors contributed equally is required to maintain and (...) strengthen collaboration. However, the phenomenon of multiple important authors can cause unfair comparisons among the research contributions and abilities of authors of different papers. This loophole may be exploited. Normalizing the credit assigned to a given paper’s authors is an easy way to solve this ethical dilemma. This approach enables fair comparisons of the contributions by the authors of different articles and suppresses unethical behaviour in author listings. Bibliometric researchers have proposed mature methods of normalized paper credit assignment that would be easy to use given the current level of computer adoption. (shrink)