Bibliographic record
Abstract
The interviews found in this volume -and the many others for which we regrettably had no space -were facilitated by numerous people.We are grateful to all those officials and politicians who talked to us and answered our queries and who permitted us to make use of their thoughts and recollections.A full list of the interviews we conducted at the end of the 1980s is printed in the bibliography of Pirouette.The complete texts of the interviews can be found in Robert Bothwell's papers at the University of Toronto archives and in the archives of the Canadian War Museum in Ottawa.Only a very few of the interviews remain closed to researchers.We have also made use here of an interview that was done for us by Paul Litt.In addition, Bothwell did some interviews with John Kirton in Washington, and Granatstein did interviews in Beijing with Bernie Frolic.The authors cheerfully admit that they are of a certain generation, the generation that seeks the assistance of the young to find its way through the electronic marvels invented in the 1990s and after -that is, after the interviews were done.At the time, they were typewritten on paper, by the authors directly, and eventually placed in files and deposited in archives.When we decided to create this volume, we resorted to the knowledge and artistry of Katie Davis, a graduate student in history at the University of Toronto, who transferred often barely legible typescript into a shining digital product.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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 teacher head, 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".