Bibliographic record
Abstract
This special issue includes a collection of extended and revised versions of papers presented at the 39th Italian Conference on Computational Logic1 (CILC 2024), which was held in Rome at the National Research Council of Italy on June 26–28. The Italian Conference on Computational Logic is the annual meeting of the Italian Association for Logic Programming2 (GULP – Gruppo Ricercatori e Utenti Logic Programming). The Conference, since its first edition, held in Genoa in 1986, has been an important occasion for meeting and exchanging ideas and experiences among national and international researchers and practitioners working in the field of computational logic. CILC 2024 was attended by more than 50 participants from universities and research centres all over Italy, as well as from Austria, Canada, Cyprus, Poland, Romania and the UK. The conference featured 34 presentations, including three invited talks, one tutorial, original contributed papers and papers already published in related venues, covering many aspects of computational logic from fundamental and theoretical results to applications, experimental experiences and system descriptions.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.103 | 0.040 |
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".