Bibliographic record
Abstract
Many people and a score of institutions played central roles in this project, from the creation of the first edition to the publication of this second edition.First and foremost, I owe an enormous and unpayable debt to my deceased wife, Glenda Thomson, for her unwavering confidence in my ability to complete this book over its ten years of gestation.Her skill in editing and her judgment on matters of language were an invaluable source of firm, clear-headed advice.Today as I write and edit, I still hear -and follow -her words of advice.We were, and in many ways continue to be, co-travellers on a remarkable journey.I thank my parents, Harold and Macrena, posthumously, for their unconditional love and their irrepressible optimism about my future when I was still young and finding my way.Similarly, I thank the rest of my family for constant encouragement.I single out Philip Cercone, executive director at McGill-Queen's University Press.In 2004, he took a chance on me, a new academic and an unknown author.With insight, he understood the issues in this book and why it should be published.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.199 | 0.139 |
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".