Bibliographic record
Abstract
A work such as this needs the efforts of many people to bring it to fruition.We have been fortunate to have that support.From its inception through the many stages in the development of the book, Professor Ann P. Zimmerman has been a constant source of encouragement and inspiration, especially to Betty Roots, and to her we extend a very special thanks.Without the unremitting work of our project manager, Sherry Pettigrew, this book never would have gone to press.Sherry gave of herself unstintingly and through it all maintained her cheerful sense of humour.Thank you, Sherry.We appreciate the support of Kathy Carter, Executive Secretary of the Royal Canadian Institute throughout.The maps are a unifying component of the volume.With the exception of the historical maps, they were developed by Conrad Heidenreich and produced by Carolyn King at York University.Further development of the maps was provided by Eric Leinberger.We are greatly indebted to all of them.We wish to thank our authors most sincerely both for their contributions and their patience in responding to our frequent requests for more information.True to the fine tradition of the RCI they are forgoing royalties.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.372 | 0.303 |
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".