Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The papers in this volume were presented at the Thirty-Fourth Annual ACM Symposium on Theory of Computing (STOC2002), held in Montreal, Quebec, Canada, May 19-21, 2002. The Symposium was sponsored by the ACM Special Interest Group on Algorithms and Computation Theory (SIGACT).In response to a call for papers, 287 paper submissions were received. All were submitted electronically. The program committee conducted its deliberations electronically, via an on-line meeting that ran from January 10 to January 19. The committee selected 91 papers from among the submissions. The submissions were not refereed, and many of these papers represented reports of continuing research. It is expected that most of them will appear in a more polished and complete form in scientific journals.The papers encompassed in wide variety of areas of theoretical computer science. The topics included algorithms and computational complexity bounds for classical problems in algebra, geometry, topology, graph theory, game theory, logic and machine learning, as well as theoretical aspects of security, databases, information retrieval, and networks, the web, computational biology, and alternative models of computation including quantum computation and self-assembly.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it