Bibliographic record
Abstract
While growing up in Vancouver in the late 1940s, I remember vividly taking my homemade panini sandwiches out of my lunch bag and being embarrassed.My sandwiches were brimming with a combination of egg, tomatoes, cheese, peppers and prosciutto, whereas most of my Anglo-Saxon classmates had neat and tidy store-bought white bread with slim fillings.It was an awkward feeling: my sandwich being out of step with the "majority sandwich" gave me a sense of not belonging.Later, while an undergraduate at the University of British Columbia in the 1950s, I had a disturbing conversation with my economics statistics professor, Tadek Matuszewski, for whom I was working as a teaching assistant.He asked me one day what I wanted to do with my life, and I answered that I wanted to go to law school.To my surprise, Matuszewski said I should not do that; and when I asked him why, he replied that I did not have the right name to be a lawyer.Granted, my name was, like his, difficult to spell and pronounce, but I did not understand why that should disqualify me from pursuing legal studies.Upon seeing my consternation, Matuszewski suggested that we make an appointment to see John Deutsch, then chair of the combined political science and economics department at ubc (later to become vicechancellor of Queen's University).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.334 | 0.174 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".