Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
Bibliographic record
Abstract
Welcome to CASCON 2008 --- the 18th Annual International Conference hosted by the IBM Centers for Advanced Studies. The CASCON Meeting of Minds conference series provides computer science and software engineering academics and professionals the opportunity to explore some of the exciting research that is underway in Canada and around the world. This year we had many interesting submissions that describe innovative approaches and fresh perspectives. CASCON 2008 attracted 73 submissions of papers from a wide variety of places. Of the 208 authors who were listed on the submitted papers, 128 (62%) came from Canada, 28 (13%) from Europe, 27 (13%) from Asia, 18 (9%) from the US, and 7 (3%) from South America. Measuring by institutions, 158 (76%) of the authors were from academic or government research institutes and 50 (24%) from industry. We followed a rigorous process to ensure that accepted papers met a standard of high quality. Each paper was reviewed by at least three members of our Program Committee, followed by a three-week on-line discussion period. Ultimately, we accepted 23 papers (32% acceptance rate) and recommended to authors of 14 papers to submit their work to the Posters track. The resulting program features three sessions on Software Engineering, two sessions on Systems, and a session each on Web Applications, Databases and Compilers. We hope you will take the opportunity to attend the Papers sessions following the morning keynotes.
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.099 | 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".