Bibliographic record
Abstract
Dave Sahota, class of 1999, grew up in Ottawa and didn’t feel like the school there offered enough opportunities or challenges. IMSA seemed like an obvious choice to expand learning opportunities and find other like-minded students. In eighth grade Sahota visited IMSA on a field-trip and was excited by the labs and the students he saw. On arrival sophomore year, fall 1996, he recalls immediately being thrown into new experiences. He played soccer and baseball and got involved in math team, chess club, and Club Pseudo. He also recalls the excitement of in-room networking, his first exposure to an internet connection faster than dial-up. In terms of classes, he particularly liked physics and math. However, he found the humanities classes more engaging and challenging than he’d expected, particularly Dr. Kiely’s history class and English with Pat McWilliams. The English classes overall sparked his interests in reading. Senior year, Sahota took a number theory class with Dr. Fogel and it led him to eventually do a Ph.D. in math. He struggled in the class, but the challenge helped him see what he could do. Overall, he appreciated the IMSA curriculum’s general approach to learning through practical problem-solving and collaboration. After graduating from IMSA Sahota attended the University of Illinois at Chicago. His classes at IMSA had given him a solid grounding and helped make the transition to college easier. After undergrad, Sahota stayed at UIC and completed a Ph.D. in math. He worked in quantitative finance for several years and recently completed a master’s in computer science. He currently works as a data scientist at Facebook. He also teaches occasional classes and looks back to his IMSA classes as a model for teaching. Interviewer: Sara Goek. Duration: 24:32
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.278 | 0.127 |
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".