Bibliographic record
Abstract
A survey of parts of General Coalgebra is presented with applications to the theory of systems.Stress is laid on terminal coalgebras and coinduction as well as iterative algebras and iterative theories. Preface: Is this Really an Introduction to Coalgebra?For my series of lectures on coalgebra on the preconference to CTCS 2002 I prepared a short text called "Introduction to Coalgebra" that was intended to cover the material of the series.I was not worried about the the choice of topics: they were given by the intentions of the course.Two years later the Program Chair Rick Blute suggested that I could publish the text in the proceedings-and after I agreed, I found myself in a fix: what topics to choose?If I add to the existing text the minimum of material that would indeed constitute a general introduction to coalgebra, the text would grow immensely.Besides, excellent introductions exist already, see e.g.[R1], [G], [JR].I decided for a minimalistic approach: in the following sections I try and present an introduction to the parts of coalgebra which are those "dearest to my heart", and to which I have also contributed.Thus, a substantial part of coalgebra topics is not mentioned at all; I hope the reader will enjoy what I present below, and I ask her or him not to take a missing topic as a message of any kind.Almost all proofs are omitted, with precise references provided, just some simple, instructive proofs are left.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.100 | 0.041 |
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".