Double-Blind Review

In a double-blind review, submissions are evaluated in such a way that neither the submitters know the identity of the reviewers nor vice versa. This mutual anonymization is intended to reduce bias – for example, regarding gender, institution, or academic reputation.

Also known as: Double-blind peer review

Reduces: Bias regarding gender, institution, career stage

Demanding due to: Anonymization of texts

What is a double-blind review?

In a double-blind review, neither the submitters know who is reviewing their work, nor do the reviewers know whose work they are evaluating. It is the strictest common form of peer review and is considered the gold standard in many scientific communities – especially where reputation effects and bias are considered critical.

Why double-blind?

• Reduction of bias: Studies show that identical submissions are evaluated differently depending on whether the name of a well-known or unknown researcher is attached to them
• Gender fairness: Anonymous assessment is proven to reduce the systematic disadvantage of women
• Institutional neutrality: Submissions from less renowned institutions receive fair opportunities
• Career stage equality: Early-career researchers are not devalued due to a lack of reputation
• Geographic fairness: International submissions from less visible research regions are evaluated on an equal footing

Practical implementation

The biggest hurdle in the double-blind process is consistent anonymization. The system must address this on two levels:

1. At the submission level: Identifying fields (author name, affiliation, funding acknowledgments) are recorded separately and are not displayed to reviewers
2. In the submission text itself: Submitters must avoid self-references ("In our previous work..." → "In Mustermann (2023)..."), unique datasets, methodological peculiarities, or grant numbers are potentially identifying

Organizers can use Converia to provide anonymization checklists to create the most effective anonymization guidelines possible.

Limitations of the process

• No complete anonymity: In small research fields, authors can often be guessed by their style or topic
• Higher effort: For both submitters and the platform
• No reduction of methodological bias: Those who prefer a specific methodological school will judge accordingly – regardless of anonymity
• Weaker conflict checking: Reviewers only recognize conflicts of interest (co-authorship) through topic similarity, not through names

Workflow in the platform

1. Submission takes place with separately recorded personal fields and submission text
2. Organizer instructions to submitters on what to look out for
3. Delivery to reviewers without identifying fields
4. Evaluation by reviewers without knowledge of the submitters
5. Aggregation of reviews by the program committee
6. Decision – identities are only disclosed to reviewers afterwards (or not at all)

Best practices

• Communicate clear anonymization guidelines in the Call for Papers
• Provide templates for submitters (e.g., "[ANONYMIZED]" as a placeholder)
• Multiple reviewers per submission to average out individual bias
• Conflict checking via topic overlap, not just names
• Reflect evaluation results back to submitters with reviewer comments (anonymized)

Double-blind review in Converia

Converia supports full double-blind workflows: automatic removal of identifying fields during delivery to reviewers, instructions to submitters for self-anonymization, and background conflict checking

Ensuring fair peer review through structure

With Converia, you can reliably implement double-blind reviews without your program coordinators having to manually anonymize every submission.

• Peer Review – Abstracts / Program / Speakers
• Abstract Management – Abstracts / Program / Speakers
• Call for Papers (CfP) – Abstracts / Program / Speakers

Double-sided anonymization with automatic field removal