Internet-Based Peer Review emulates the academic rigor (UX) of peer-review research in a public online network. This program was designed by Afilado Tumbas in the Network Theory Applied Research Institute's Forge Laboratory.
"Taylor & Francis Author Services" describes the peer review process as a "collaborative effort" between "independent...experts in [the] field," and study authors. https://authorservices.taylorandfrancis.com/publishing-your-research/peer-review/
IBPR has two primary effects on peer-review research:
Distributed review process
Distributed expertise development
Distributed Review Processing prevents bottlenecks of expertise for research qualifications and distribution of new information to the public
Templated submission formats at UI
Universal access to consolidated journal
Distributed Expertise Development
Users will not produce media in a vacuum but in a cloud of other users contributing dialogue to the science. As conflicts over facts arise, they will be settled within the confines of the distributed network and its governing protocols. The long-term result is user base that confirms and trains new users and holds knowledgeable users accountable to new data
Full Description
The User Experience of IBPR is a social media experience with users interact with data artifacts of other users. The network protocols have two parts-- a machine learning program supporting response vectors for user and data reputation ratings and the user interface.
Data submissions are received in templated formats for allowed media types (photo, video, text, etc.). The database of submissions are searchable by keywords searching whole documents-- similar to ctrl+F in browser Find in Page function.
Comments present as templates for further scientific dialogue and linking citations hyperlink other studies inside the submissions database.
Notifications keep users informed when their submissions are interacted with and settings provide light customization options.
Citation links and LBTAS (Leveson-Based Trade Assessment System) submission ratings are vectored in a ML system to reflect submission reputation.