Bibliographic record
Abstract
This volume contains the proceedings of the Fourth International Workshop on Higher Order Operational Techniques in Semantics (HOOTS 2000). The workshop was held on 22 September 2000 in Montréal, Canada, as part of the ACM Colloquium on Principles, Logics, and Implementations of high-level programming languages (PLI 2000). These proceedings are available as Issue 3 of Volume 41 of Elsevier's Electronic Notes in Computer Science: http://www.elsevier.nl/locate/entcs/ Thanks are due to a number of people, especially the programme committee: Andrew Gordon, Microsoft Research Robert Harper, Carnegie Mellon University Alan Jeffrey, DePaul University (Chair) Andrew Pitts, Cambridge University Julian Rathke, Sussex University David Sands, Chalmers University Davide Sangiorgi, INRIA Sophia Antipolis Carolyn Talcott, Stanford University We would also like to thank the anonymous referees who helped to review the papers for this meeting. There were two invited talks for this workshop: A Second Glance at Feferman-Landin Logic by Ian Mason, University of New England, Australia (joint work with Carolyn L. Talcott, Stanford University). Weak Bisimulations by Decreasing Diagrams by Cédric Fournet, Microsoft Research (joint work with Georges Gonthier, INRIA Rocquencourt). The PLI 2000 workshops were organized by Amy Felty, University of Ottawa, and Franck van Breugel, York University. The ENTCS series is edited by Michael Mislove, Tulane University. On behalf of the participants, we would like to thank Microsoft Research for their generous sponsorship of this workshop.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.599 | 0.401 |
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".