Allows to rate scientific publications within the Readersourcing approach.

Latest Version

Version

1.0.14-alpha

Update

October 22, 2019

Size

4.88MiB

Developer

rs-server.herokuapp.com

Readersourcing 2.0: RS_Rate Extension

The main mechanism to spread scientific knowledge is the scholarly publishing process, which is based on the peer review activity; a scientific article written by some authors is judged and rated by colleagues of the same degree of competence. Although peer review is a reasonable and well established a priori mechanism to ensure the quality of scientific publications, it is not free from problems, and indeed it is characterized by various issues related to the process itself and the malicious behavior of some stakeholders.

Among the various alternative approaches to the peer review activity proposed during the last years, there are some that aim to take advantage of information which are otherwise lost. When a scientific article is published its readers have their own opinion about it, but such opinions usually remain private spread informally between a few collaborators. Eventually, they remain “encoded” inside citations inserted into other articles. It would be useful to have an approach to the peer review activity which considers also such opinions.

In literature there are two proposals which aim to take advantage of readers’ opinions by outsourcing the peer review activity to their community. The shared idea consist in asking readers to rate quantitatively the articles they read; these ratings are used to measure the overall quality of such articles as well as the reputation of a reader as an assessor; moreover, they are used to derive the reputation of a scholar as an author.

In other terms, the main issue with which the two aforementioned proposals have to deal consists in how the ratings that the assessed entity (i.e., a publication) receives should be aggregated into indexes of quality and, from these indexes, how to compute indexes of reputation for the assessors (i.e., the readers) and, eventually, indexes of how much an author is “skilled” (i.e., a measure of his ability to publish papers which are positively rated by their readers).

A working implementation of the two proposals has been provided by building the Readersourcing 2.0 ecosystem within a research project co-funded by SISSA Medialab and University of Udine. This ecosystem is an independent, third-party, non-profit, academic/scientific endeavour, aimed at quality rating of both scholarly literature and scholars and is composed of four software applications. There is one (RS_Server) which acts as a server to gather all the ratings given by readers and one that acts as a client (RS_Rate) to allow the readers themselves to effectively rate publications, although it is possible to carry out every operation also directly on a web interface pro-vided by RS_Server. There is also a component (RS_PDF) which has the task to annotate files in PDF format by taking advantage of an ad hoc software library; this component is exploited by the server-side application. Lastly, there is also an additional component (RS_Py) which provides a fully working implementation of the two proposals that can be used as a standalone software. The code of every software component and the overall documentation is available on GitHub (https://github.com/Miccighel/Readersourcing-2.0).

RS_Rate (i.e., this extension) allows a scholarly reader to:
- Rate a scientific publications while he is reading it;
- Annotate a scientific publication with a rating url to express his rating at a later time;
- Automatically extract the rating url from one of his annotated publications;
- Sign up on the Readersourcing 2.0 ecosystem and update his profile.

Source Manifest.json

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