GROUPLAB AT SKIGRAPH
Bibliographic record
Abstract
The Western Computer Graphics Symposium, nicknamed 'SkiGraph', is an annual professional meeting comprising mostly graphics researchers and their graduate students from Western Canada. In 2000, several Western Canadian researchers in Human Computer Interaction: Saul Greenberg (U.Calgary), Carl Gutwin (U. Saskatchewan), Kori Inkpen (Simon Fraser) and Sheelagh Carpendale (U. Calgary) agreed to use Skigraph as a way to get themselves and their graduate students together, where students would present papers describing their research. Because it was important for all graduate students to share their ideas, the papers written could range from identification of research areas and tentative proposals of research problems all the way to detailed results from mature work. This research report collects five research papers by students at Grouplab to SkiGraph (Grouplab is the laboratory for human computer interaction research at the University of Calgary). The papers are listed below. In all cases, the students are the first author followed by faculty members who have supervised or contributed to the work in one way or another. Individual papers may be cited directly by including the following information.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.532 | 0.466 |
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".