Memorial: Roger I. Simon
Bibliographic record
Abstract
We begin this new issue and this new school year, for those of us who labour in the academic enterprise—the theme of this special issue—on a sad note. Roger I. Simon, an inspiring educator, honoured colleague and long-time member of TOPIA’s advisory board, passed away on Monday, September 17, at Mt. Sinai Hospital in Toronto. In this special issue of TOPIA we rethink the academy, and so it is fitting that we begin by remembering one of the academy’s best, a teacher of teachers, as Henry Giroux reminds us in his eulogy, and a profoundly original scholar of pedagogy whose intellectual pursuits challenged us to think a different future while asking vexing questions of a most difficult past. At TOPIA, we will miss his steadfast support, his broad knowledge of cultural studies, his acuity and his wisdom, and we will miss him as a friend. In memory of Roger, we invited two of his closest friends and colleagues, Henry Giroux (McMaster University) and Deborah Britz- man (York University), to share with readers the eloquent tributes they have written and spoken in his memory.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.085 | 0.050 |
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".