In Memoriam: Susan Christopherson (1947–2016)
Bibliographic record
Abstract
Susan was a dear and long-standing friend and colleague. I first met her in the first year of my Master’s studies at Berkeley in 1977, when I enrolled in Allan Pred’s human geography seminar, along with Susan, Michael Storper and a small handful of equally interesting and stimulating colleagues. Even at that very early stage in her academic career, Susan struck me as someone who was unusually poised, experienced and remarkably wise beyond her years. She was also unfailingly warm, open and generous—both intellectually and personally. Perhaps because we shared a northern upbringing—hers in Minnesota, mine in Ontario—we naturally gravitated towards one another. Her Nordic roots and Minnesota perspective seemed to confer upon her a lifelong fascination with—and affection for—all things Canadian. I think she secretly felt that Canada, with its public healthcare and its more fully elaborated welfare state, represented a kinder, gentler place that her own country might one day become. She was fond of saying “Of course, you Canadians have this all figured out” or “We have so much to learn from you Canadians”. In fact, Susan and I shared many other affinities besides our northern roots and affection for all things Canadian. As scholars, we both came to appreciate the value of comparative approaches to social science. Susan had a deeply developed sense of the intellectual gains to be made by comparing social systems in different political economies. And she adopted this as a central device in so much of her work.
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.074 | 0.049 |
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".