Bibliographic record
Abstract
There are currently four active ACM SIGGRAPH chapters in Canada. We decided to get together to write up a review of our respective ACM SIGGRAPH chapter activities, because these activities may be of interest to the Canadian and overall ACM SIGGRAPH community. The Canadian chapters include: Atlantic Heather Fowler: Chair http://atlantic-canadian.siggraph.org Montreal Myriam Côté, Chair http://montreal.siggraph.org Toronto Adele Newton, Chair http://toronto.siggraph.org Vancouver Andrew Woo, Chair http://vancouver.siggraph.org If you are interested in being a volunteer or a speaker (especially if you are from out of town), please contact the appropriate chapters (see addresses above), and we will be more than happy to talk to you. We hope you enjoy the review of the ACM SIGGRAPH Canadian chapters!
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.214 | 0.129 |
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".