New Task Types at the Canadian Computing Competition
Bibliographic record
Abstract
Abstract. In the 2006 competition workshop held at Dagstuhl, Germany, there were many fruitful discussions about the difficulties facing computer science competitions today. Our competitions have several purposes: to foster interest in the discipline, to create a community, and to promote achievement, for example. Balancing these various purposes may require many tradeoffs. Several participants identified areas where we need to improve our competitions. Tom Verhoeff (2006) discussed the problem of giving a meaningful ranking to incorrect solutions. Maryanne Fisher and Tony Cox (2006) pointed out that some groups of students are disadvantaged by the present format. Many participants made suggestions for improving the competitions. One of the suggestions in our paper (Cormack et al., 2006) was open-ended tasks. A task is open-ended if there is no known optimal solution to the problem. Points are awarded for correct submissions in proportion to how well they do. A vast number of real-world applications, such as pattern recognition, information retrieval, and compiler optimization appear suitable for this purpose. At Canada’s national informatics olympiad, the Canadian Computing Competition, we have been exploring several of these suggestions. In this paper we describe the experiments we have performed and we analyze whether the objectives have been achieved. Key words: computing competitions, open-ended tasks, informatics in Canada. 1.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
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".