AMOR: Ambiguous Authorship Order

Abstract: As we all know, writing scientific papers together with our beloved colleagues is a truly remarkable experience (partially): endless discussions about the same useless paragraph over and over again, followed by long days and long nights -- both at the same time. What a wonderful ride it is! What a beautiful life we have. But wait, there's one tiny little problem that utterly shatters the peace, turning even renowned scientists into bloodthirsty monsters: author order. The reason is that, contrary to widespread opinion, it's not the font size that matters, but the way things are ordered. Of course, this is a fairly well-known fact among scientists all across the planet (and beyond) and explains clearly why we regularly have to read about yet another escalated paper submission in local police reports. In this paper, we take an important step backwards to tackle this issue by solving the so-called author ordering problem (AOP) once and for all. Specifically, we propose AMOR, a system that replaces silly constructs like co-first or co-middle authorship with a simple yet easy probabilistic approach based on random shuffling of the author list at viewing time. In addition to AOP, we also solve the ambiguous author ordering citation problem} (AAOCP) on the fly. Stop author violence, be human.

• The paper introduces the AMOR system, which randomizes author order to equalize credit distribution among coauthors.
• It employs a probabilistic permutation method, enabling any author to appear in any position with equal likelihood.
• The dynamic approach could resolve longstanding disputes in academic publishing, though implementation faces technological challenges.

Essay on "Solving the Author Ordering Problem: AMOR System"

This paper addresses the longstanding issue in academic publishing regarding the Author Ordering Problem (AOP), a contentious area often fraught with personal and professional ramifications. The authors of this paper propose an innovative system, AMOR, which utilizes a probabilistic approach to solve this issue by randomly shuffling the author list at viewing time. By employing this method, the order of authorship is dynamically altered, potentially democratizing the perception of contribution and credit related to a publication.

Methodology

AMOR is fundamentally straightforward in its operations. The system generates a random permutation of a given list of authors each time the publication is viewed. For a list with $n$ authors, this permutation allows for $n!$ possible configurations, thereby treating each author with an equal probability of appearing in any position within the list. This method effectively eliminates disputes of order by removing static authorial hierarchies and providing an equal platform for recognition among contributing authors.

Strengths and Limitations

The paper articulates several limitations of the AMOR system, which primarily relate to technological adoption. Current viewing platforms capable of supporting the animations necessary for visualizing AMOR are limited, with only specific PDF viewers such as Adobe Acrobat Viewer and Okular being mentioned as compatible. Furthermore, due to significant reliance on publisher technology for implementation, widespread adoption could require substantial time investments. Moreover, the system does not support traditional printed documents.

Implications and Speculation

The implications of AMOR are multifaceted, affecting both practical and theoretical aspects of authorship attribution in academia. Practically, should AMOR gain traction, it could revolutionize how credit is distributed amongst authors, potentially reducing conflicts regarding contribution recognition. Theoretically, the implementation of such a system might also stimulate further investigations into other standardized aspects of publication that could benefit from similar probabilistic approaches.

Moreover, the system's ability to dynamically alter author appearance could mitigate issues such as conflicts of interest or disputes over collaborative work. As AI continues to evolve, AMOR hints at a future where machine learning and automated systems could play more substantial roles in not only the research process but also in research dissemination and collaboration dynamics.

Conclusion

While the AMOR system presents a novel method for resolving the Author Ordering Problem, its practical implementation requires advancements in publisher technology and viewer capability. Nevertheless, AMOR's approach aligns with contemporary trends in AI and automation, indicating a potential paradigm shift in academic publishing practices. Further exploration and refinement of this methodology could contribute significantly to more equitable and transparent authorship practices in scientific research.