Bibliographic record
Abstract
Let C be a category with finite colimits, and let (E, M) be a factorisation system on C with M stable under pushout.Writing C; M op for the symmetric monoidal category with morphisms cospans of the form c → m ←, where c ∈ C and m ∈ M, we give a method for constructing a category from a symmetric lax monoidal functor F : (C; M op , +) → (Set, ×).A morphism in this category, termed a decorated corelation, comprises (i) a cospan X → N ← Y in C such that the canonical copairing X + Y → N lies in E, together with (ii) an element of F N .Functors between decorated corelation categories can be constructed from natural transformations between the decorating functors F .This provides a general method for constructing hypergraph categories-symmetric monoidal categories in which each object is a special commutative Frobenius monoid in a coherent way-and their functors.Such categories are useful for modelling network languages, for example circuit diagrams, and such functors are useful for modelling their semantics.I thank John Baez, Sam
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".