Existing Models
Taylor & Francis provide a nice overview of the 5 most prominent existing peer review models.
Can we design a new model that best balances the tradeoffs inherent in the existing models?
The academic community has long relied on several established peer review models, each attempting to balance transparency, fairness, and quality of feedback. Single-anonymous review aims to enable candid feedback through reviewer anonymity. Double-anonymous review strives to minimize bias by concealing both author and reviewer identities. Open peer review promotes transparency and accountability. Post-publication review seeks broader community input.
Despite their merits, these models reveals fundamental tradeoffs and all come up short in addressing them. When reviewers remain anonymous, some may provide hasty or nonconstructive criticism without accountability. When identities are known, social and professional pressures can constrain honest feedback. The binary accept/reject paradigm often fails to capture nuanced scientific value. Perhaps most critically, these models frequently operate in isolation, missing opportunities for broader scientific dialogue.
These challenges don't just inconvenience researchers – they fundamentally impact the pace and quality of scientific progress. Biased or superficial reviews can delay or prevent the publication of valuable work, a problem addressed by the "publish, then open review" models that a few publishers have began to partially adopt including F1000Research, eLife, and Gates Open Access. Conversely, problematic research may pass through peer review without sufficient scrutiny (e.g., as monitored by Retraction Watch).
Our view is that these issues derive from a fundamental economic incentives problem: the current system relies on a small number of unpaid reviewers to evaluate increasingly complex research. This economic model cannot scale to meet the growing needs of modern science. As research becomes more interdisciplinary and data-intensive, thorough evaluation often requires multiple experts examining different aspects of the work. Yet without compensation or public recognition, most scientists will only dedicate limited time to peer review, leading to superficial evaluation and lengthy delays.
The C-SQD platform introduces a new paradigm that fundamentally shifts the economics of publishing by creating a marketplace for review elements – discrete evaluations of specific aspects of a manuscript. By compensating reviewers for these contributions, we enable deeper, more thorough evaluation of research while respecting reviewers' time and expertise.
Our two-phase review process combines the benefits of broad community input with focused expert synthesis. The double-blind public review phase enables diverse perspectives while maintaining anonymity. The expert synthesis phase ensures deep, specialized evaluation of significant work.
This approach is enhanced by innovative features that address longstanding peer review challenges:
- Our stochastic review element solicitation algorithm matches manuscripts with qualified reviewers and includes reviewer compensation
- Multi-dimensional evaluation tuples together with the "publish, then review" model preserve important distinctions that would be lost in simple accept/reject decisions
- Synthesis reviews and review elements become part of the scientific record, enabling reviewers to be better recognized for their essential role in the scientific process
- Self-governing reviewer communities enable field-specific standards while maintaining connections to broader scientific discourse
- All review elements and expert synthesis reviews are re-identified after the initial review period ends
By properly valuing reviewer contributions and enabling broader participation in the review process, C-SQD aims to significantly increase the reliability of published research. Our platform recognizes that thorough peer review is essential scientific work that deserves compensation, not just a professional obligation.