A Brief Survey on the Categorical Semantics for Geometry of Interaction In Memory of Phil Scott
Bibliographic record
Abstract
Philip Scott, better known to his friends and colleagues as Phil, was a Professor of Mathematics at the University of Ottawa (Canada). Sadly, Phil passed away 1 after a long battle with cancer - which he bravely fought till the very end - on 18 December 2023 at the age of 76. Phil was born on 27 December 1947 in Leeds (UK). When Phil was still a baby, his family moved to North Carolina (USA), where he spent his childhood. He went on to study mathematics at the University of North Carolina at Chapel Hill (USA), before moving to Canada in the 1970s. Phil did his Ph.D. at the University of Waterloo (Canada), supervised by Denis Higgs, and obtained his doctorate in pure mathematics in 1976. Phil joined the Mathematics Department at the University of Ottawa in 1982, eventually becoming Professor Emeritus, and remained there until his passing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".