Proceedings of the 14th Western Canadian Conference on Computing Education
Bibliographic record
Abstract
Welcome to the 14th Western Canadian Conference on Computing Education (WCCCE 2009)! Thanks to Simon Fraser University for hosting the Western Canadian Conference on Computing Education for the first time. I would like to thank all members of the WCCCE 2009 Steering Committee, all members of the Conference Committee, all members of the Local Organizing Committee, as well as all authors and panelists, reviewers, session chairs, exhibitors, workshop organizers, sponsors, keynote speakers, and the invited speakers for their constant support in organizing this conference. It was very encouraging to receive a large number of high quality submissions for papers, panel discussions, and workshops from all over Canada, USA, and the rest of the world. The Conference Committee members and the reviewers faced quite a challenge deciding what to accept/reject for presentation. It gives me a lot of pleasure that we will be joined by four speakers of national and international fame. I am sure that all delegates will enjoy the very informative presentations by our two keynote speakers (Dr. Bjarne Stroustrup & Dr. Gary Poole) and two invited speakers (Dr. Yan Xu and Mr. Paul Denny) on Friday and Saturday. Thanks to all speakers, once again! We are delighted that for the first time in the history of this conference, WCCCE will run in cooperation with ACM and SIGCSE. As part of this in cooperation status, the proceedings will be included in the ACM Digital Library.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.367 | 0.181 |
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".