With the con­stant pres­sure on aca­d­e­mics to pub­lish research papers, there is a temp­ta­tion for research groups to include “coau­thors” who have not really made any con­tri­bu­tion to the man­u­script. This seems more preva­lent in some fields (e.g., the health sci­ences) than oth­ers. Occa­sion­ally, I am asked to add an author to a paper that has already been accepted for pub­li­ca­tion in the Inter­na­tional Jour­nal of Fore­cast­ing. I am very reluc­tant to do this as it is hard to imag­ine how some­one could be left off a paper while it goes through sev­eral revi­sions, only to be remem­bered after the paper is accepted. It looks like a last ditch attempt to get some­one a pub­li­ca­tion rather than a gen­uine research con­tri­bu­tion. Most uni­ver­si­ties now have an author­ship pol­icy. The author­ship pol­icy at Monash Uni­ver­sity includes the fol­low­ing state­ments. Attri­bu­tion of author­ship … in all cases author­ship must be based on mak­ing a sub­stan­tial intel­lec­tual con­tri­bu­tion to the work described and tak­ing sole or joint respon­si­bil­ity for that con­tri­bu­tion or, where appro­pri­ate, the work as a whole. Accord­ingly, author­ship must be based upon a sub­stan­tial con­tri­bu­tion and respon­si­bil­ity for at least one, and usu­ally more than one, of the fol­low­ing activ­i­ties: Con­cep­tion and design of the project; Analy­sis and inter­pre­ta­tion of research data; Draft­ing

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It seems that most jour­nals no longer do any seri­ous copy-​​​​editing, and the stan­dard of Eng­lish is falling. Today I was read­ing an arti­cle from the Euro­pean Jour­nal of Oper­a­tional Research, which is a sup­pos­edly a good OR jour­nal (cur­rent impact fac­tor over 2). Take this for an exam­ple from the first page of this paper: If the learned pat­terns are unsta­ble, the learn­ing tools would pro­duce incon­sis­tent con­cepts. To over­come this dif­fi­cult sit­u­a­tion, we employed arti­fi­cial neural net­works (ANNs, NNs) for help­ing the learn­ing task. NNs have attracted a lot of atten­tion form aca­d­e­mic researchers and indus­trial prac­ti­tion­ers because of the pow­er­ful flex­i­ble non­lin­ear mod­el­ing capa­bil­ity ([Balestrassi et al., 2009], [Bellini and Figa-​​​​Talamanca, 2005] and [Qi and Zhang, 2001]). It is the main rea­son for their pop­u­lar­ity that the data dri­ven tools have less restric­tion when applying. Learning tools with the sta­ble train­ing base usu­ally have reli­able per­for­mances. The paper con­tin­ues in this vein for ten pages, cul­mi­nat­ing in an equally remark­able con­clu­sion: With the sam­ple size grow­ing, the shadow set con­tains a large num­ber of func­tional, vir­tual data, instead of whole real data. It would pos­sess less pop­u­la­tion rep­re­sen­ta­tion then. Before estab­lish­ing the the­o­ret­i­cal basis, we used the trial-​​​​and-​​​​error way for the expe­di­ent expla­na­tion and con­cluded

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