Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Bibliographic record
Abstract
It's a pleasure to welcome you to OOPSLA 2006, the 21st Annual Conference on Object-Oriented Programming, Systems, Languages, and Applications. OOPSLA is the premier forum for practitioners, researchers, and students in diverse disciplines whose common threads are objects and related technologies. From its inception, OOPSLA has served as an incubator for advanced technologies and practices. Dynamic compilation and optimization, patterns, refactoring, aspect-oriented software development, agile methods, service-oriented architectures and model-driven development (to name a few) all have OOPSLA roots.OOPSLA 2006 continues and strengthens that tradition. It features an exciting roster of researchers and practitioners from around the world coming to showcase their latest work in a highly diverse set of forums that meet the needs of our equally diverse audience. Presentations from invited speakers dovetail with technical papers, practitioner reports, expert panels, demonstrations, formal and informal educational symposia, workshops, and diverse tutorials from world-class lecturers. Bend your head around some out-of-the-box thinking at the ever-popular Onward! track. Discuss late-breaking results with the researchers themselves at poster sessions, which culminate in the Fifth Annual ACM Student Research Competition. Get some hands-on design experience at the expert-mentored DesignFest®. Gather together with like-minded people at Birds-of-a-Feather sessions to discuss shared topics of interest. Or just let us know what's on your mind at the Lightning Talks, where anyone can speak to the community on just about anything at all.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.098 | 0.046 |
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".