Proceedings of the 15th Western Canadian Conference on Computing Education
Bibliographic record
Abstract
Welcome to the 15th Western Canadian Conference on Computing Education (WCCCE 2010)! It was very encouraging to receive a large number of high quality submissions for papers, panel discussions, and workshops from all over Canada, 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 have been successful to invite four keynote speakers of national and international fame from Canada and the United States. I am sure that all delegates will enjoy the very informative presentations by the keynote speakers Dr. Marcia C. Linn (University of California, Berkeley), Dr. Donald Chinn (Institute of Technology at the University of Washington), Dr. David Kaufman (Simon Fraser University, Burnaby), and Don Slater (Carnegie Mellon University). Since last year, WCCCE runs in cooperation with ACM and SIGCSE. As part of this in cooperation status, the proceedings will be included in the ACM Digital Library. This year, we succeeded in getting funding from the ACM outreach program to bring the Alice workshop to the conference.
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 | 0.002 | 0.004 |
| 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 | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.358 | 0.140 |
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".