The document explores various peer review models in journal publishing, outlining single-blind, double-blind, triple-blind, open, post-publication, collaborative, and pre-print peer reviews. Each model has distinct benefits, such as minimizing biases, promoting transparency, or enabling early feedback. Additionally, it highlights the use of machine learning in streamlining the peer review process.

1. Sarasadat Makian
Exploring different types
of peer reviews in
journal publishing
A Guide for PhD students and Early Career Researchers

2. Sarasadat Makian
Single-Blind Peer Review:
In this traditional approach, the
reviewer knows the identity of the
author, but the author remains
unaware of the reviewer's identity.
This method aims to maintain the
impartiality of the reviewer.

3. Sarasadat Makian
Double-Blind Peer Review:
In this model, both the author and
the reviewer remain anonymous to
each other. It minimizes biases
related to the author's reputation,
gender, or affiliation and is widely
considered a gold standard for
objective evaluation.

4. Sarasadat Makian
Triple-blind peer review:
This is a variant of the double-blind
peer review process. In this
process, the identity of not only the
authors, but also the editors
handling the manuscript, is
concealed from the reviewers. In
addition, the identity of the
reviewers is concealed from the
authors.

5. Sarasadat Makian
Open Peer Review:
This model is characterized by
transparency, where the reviewer's
and author's identities are known to
each other. This approach can
promote a constructive dialogue
between authors and reviewers
and may enhance accountability.

6. Sarasadat Makian
Post-Publication Peer Review:
Some journals opt for publishing
research immediately and then
allowing peer review to happen post-
publication. This model encourages
community involvement and scrutiny,
but it also requires robust and
efficient mechanisms for updating
and revising published work.

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Collaborative Peer Review:
This emerging model involves
multiple reviewers working
together on a manuscript. It can
help to reduce individual biases and
provide a more holistic evaluation.

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Pre-Print Peer Review:
With the rise of pre-print servers,
peer review can occur before
formal submission to a journal.
Authors can receive feedback and
improve their work before seeking
publication.

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Collaborative Peer Review
Platforms:
There are platforms where
researchers can post their work
for open feedback and peer review
before submitting it to a journal.
Examples include PubPeer and
F1000Research.

10. Sarasadat Makian
Machine learning-assisted
peer review:
In some cases, machine learning
algorithms are used to help with the
initial selection and matching of
manuscripts with reviewers,
streamlining the process.