Bibliographic record
Abstract
T his book would never have come to fruition without a great deal of help from many quarters.First on that list is Allan Ronald.Allan is, of course, a central character in the story, it being his connections in 1980 with Nairobi's Herbert Nsanze that started the scientific collaboration which became the tale the book tells.But the book itself would not have happened without Allan's energy and generosity.From the moment I approached him with the idea of setting down the history of what I have always thought was an extraordinary episode in not just Canadian but global scientific research-and around the most explosive epidemic of our times-he was enthusiastic.He provided all his information, introduced me to other vital sources and, most important, shook the money trees for the support I needed for research and writing.Allan, Frank Plummer, and Stephen Moses not only met with me many times to explain their work and how the story, as they saw it, had unfolded, but they also double-checked my material to ascertain that what I had was accurate, not only historically but also medically and scientifically.Many others gave selflessly of information and perspective, notably Joanne Embree and Ian Maclean.At the administrative end, Carol Sigurdson and Stacey Zazula were on
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.426 | 0.361 |
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".