Introduction
C-SQD seeks to foster a flexible platform for scientific discourse that can operate across diverse fields and methodologies. Unlike traditional journals with fixed identities and editorial boards, C-SQD envisions an environment where reviewer communities can emerge naturally. The notion of a “community” in this context is not strictly defined. Instead, it can be viewed as a fluid collection of reviewers who share some identifiable traits, interests, or approaches. The core question is how these communities interact with the platform’s manuscript evaluation system, which represents each work via a five-dimensional tuple (N, M, P, L, C). Should communities be fixed and clearly labeled, or should they form and dissolve freely? How do we ensure that the complexity of knowledge and the openness to criticism championed by Popper & Deutsch are upheld?
In this post, we examine alternative models for integrating communities with the manuscript evaluation tuple. After reviewing several approaches, we present a model in which communities operate as tags—flexible descriptors that can be applied to reviewers. This refinement simplifies the formation, reformation, and dissolution of communities and aligns closely with an open, dynamic philosophy of knowledge.
The Manuscript Evaluation Tuple
C-SQD evaluates manuscripts using:
• N : Reported non-ethical problems
• M : Reported ethical problems
• P : Perceived significance
• L : Depth of review scrutiny
• C : Citation impact
This tuple avoids reducing a manuscript to a single score and instead presents multiple aspects of its reception and quality. Still, different groups of reviewers may weigh these aspects differently. The platform thus considers how to recast the tuple from a community perspective.
Alternative Approaches Considered
We initially considered several configurations for connecting communities and the evaluation tuple:
Fixed, Labeled Communities
Here, communities would mirror traditional journals or professional societies, with stable identities and recognized memberships.
Pros:
• Provides stable reference points.
• Familiar to users accustomed to established journals.
Cons:
• Risks entrenching viewpoints, limiting critical exchange.
• Requires maintenance as fields evolve.
Partial Specialization of Certain Metrics
Another idea was to apply the community lens selectively, for example, recomputing P for specific communities while leaving N, L, S global.
Pros:
• Less complexity than fully recomputing all metrics.
• Addresses the most subjective metric (significance) field-by-field.
Cons:
• Still a hybrid system that can confuse users.
• Preserves a top-down notion of certain metrics as universal.
Weighted Interpretations of the Tuple
Communities could define weighting schemes to reinterpret the global tuple, emphasizing or de-emphasizing certain dimensions.
Pros:
• Avoids complex recalculations.
• Simple to implement.
Cons:
• Implies aggregation, losing the multidimensional clarity of the tuple.
• Does not allow genuinely distinct community-based computations.
Dynamic, Overlapping, or Emergent Communities
The final model considered is one where communities emerge based on user-defined filters rather than fixed affiliations. Initially, this was conceived as sets of reviewers defined by field, methodology, or interests. Over time, we refined this approach so that communities become simply another form of reviewer tagging.
Pros:
• Highly flexible and adapts as fields or interests change.
• Avoids entrenchment of views, allowing communities to emerge, grow, and dissolve
naturally.
Cons:
• Complexity: users must navigate many possible views.
• Less stable reference points, potentially increasing interpretive effort
Philosophical Context
From a Popperian perspective, knowledge evolves through the criticism and testing of ideas. Fixed, recognized communities risk insulating certain frameworks from sustained critical engagement. A dynamic model, by contrast, lowers the barriers to creating new intellectual coalitions and dismantling old ones. It mitigates the formation of stagnant networks and encourages ongoing interaction across boundaries.
Refining the Dynamic Model: Communities as Tags
In the chosen dynamic model, both “communities” and other reviewer attributes (e.g., reviewer credentials including highest degree attained, field specializations, methodological keywords, thematic focuses) are simply tags assigned to reviewers. Certain tags require verification, e.g., highest degree attained. Platform users can then:
1. Apply filters that select reviewers based on any combination of tags, including those representing established communities, emerging interest groups, or other criteria.
2. Dynamically recompute (N, M, P, L, C) using only reviews from the selected subset.
This means communities are not separate, privileged structures. A self-organizing community would be defined when a group of reviewers adopt a certain tag (e.g., ’computational oncology group’). With no additional platform logic, that tag can be combined with other filters. For example:
• ’computational oncology group’ + ’machine learning’ tags to focus on a niche intersection.
• ’theoretical high energy physics society’ tag combined with ’5+ completed SynthesisReviews’ to find seasoned theoretical physics reviewers.
Implementing the Community-as-Tag Approach
Reviewer Profile Tagging
Each reviewer maintains a profile with one or more tags denoting their fields, methodologies, communities, and other attributes. Tags are user-generated and can proliferate freely.
User-Defined Filtering of Reviewers
When examining a manuscript, a user selects filters to define a “community lens.” The platform identifies all reviewers who match the chosen tags and recalculates:
• N : Problems (non-ethical) flagged by these reviewers
• M : Problems (ethical) flagged by these reviewers.
• P : Significance as scored by these reviewers.
• L : Depth of review counting only this subset.
• C : Citation impact from manuscripts authored by members of this subset, if desired.
Trade-Offs and Future Directions
Communities emerge when users start using certain tags collectively. If a particular tag gains prominence, it can be treated as a de facto community. If interest wanes, that community tag will appear less frequently and gradually vanish from common filters.
The dynamic, tag-based community model introduces complexity for users who must navigate many possible permutations. Yet this complexity is the source of its strength: no viewpoint is privileged or permanently fixed. The platform and its users co-create communities as needed, ensuring that no school of thought is shielded from outside criticism. Over time, patterns may emerge, and certain tags may become widely recognized as indicating stable communities. Users might compile lists of popular tags, offering easy shortcuts to well-known communities. But these remain social conventions rather than platform-enforced hierarchies.