Bibliographic record
Abstract
A.The cyclic Chu-construction for closed bicategories with pullbacks, which generalizes the original Chu-construction for symmetric monoidal closed categories, turns out to have a non-cyclic counterpart.Both use so-called Chu-spans as new 1-cells between 1-cells of the underlying bicategory, which form the new objects.Chu-spans may be seen as a natural generalization of 2-cell-spans in the base bicategory that no longer are confined to a single hom-category.This view helps to clarify the composition of Chu-spans.We consider various approaches of linking the underlying bicategory with the newly constructed ones, for example, by means of two-dimensional generalizations of bifibrations.In the quest for a better connection, we investigate, whether Chu-spans form a double category.While this turns out not to be the case, we are led to considering a generalization of the construction to paths of 1-cells in the base, leading to two hierarchies of closed bicategories, one for linear paths and one for loops.The possibility of moving beyond paths, respectively, loops of the same length is indicated.Finally, Chu-spans in rel are identified as bipartite state transition systems.Even though their composition may fail here due to the lack of pullbacks in rel , basic game-theoretic constructions can be performed on cyclic Chu-spans.These are available in all symmetric monoidal closed categories with finite products.If pullbacks exist as well, the bicategory of cyclic Chuspans inherits a monoidal structure that on objects coincides with the categorical product.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".