Double-Blind Review

In a double-blind review, submissions are evaluated in such a way that neither the authors 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 authors 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 evaluation is proven to reduce the systematic disadvantage of women
• Institutional neutrality: Contributions from less renowned institutions are given a fair chance
• Career stage equality: Early-career researchers are not penalized for 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 captured separately and are not visible to reviewers
2. Within the manuscript itself: Authors must avoid self-references ("In our previous work..." → "In Mustermann (2023)..."); unique datasets, methodological quirks, or grant numbers are potentially identifying

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

Limitations of the process

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

Platform workflow

1. Submission occurs with separately captured personal fields and contribution text
2. Organizer guidelines for 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

• Clear anonymization instructions in the Call for Papers communicate
• Provide templates for submitters (e.g., "[ANONYMIZED]" as a placeholder)
• Multiple reviewers per contribution 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 when delivered to reviewers, instructions for submitters on self-anonymization, and background conflict checking

Ensure fair reviewing through structural safeguards

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

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

Double-sided anonymization with automatic field removal