Bibliographic record
Abstract
Whenever I start reading a new book, I immediately turn to the acknowledgments.I know some people skip them over in favour of the text, but I find they are a fascinating source of academic genealogy.Good acknowledgments can enliven details about the book's backstory, offer glimpses into the author's writing odyssey, or reflect on life's personal triumphs and tragedies.But most of all, they are a humbling reminder of all the people and institutions that made writing the book possible.So, it gives me great pleasure to finally recognize all the branches of my "academic family tree."At the University of Western Ontario, I was very fortunate to be surrounded by a fantastic group of people during my time as a graduate student and parttime professor.My mentor, Jonathan Vance, is a towering figure in the field of Canadian history, and I am eternally grateful for his encouragement and guidance while pursuing this somewhat unconventional project, especially during its formative stages.When I first told him that I wanted to change my focus from veterans' rehabilitation after the Second World War to "war garbage, " he raised an eyebrow but never any doubts about the idea.I think the topic was just weird enough to pique his interest, and over the next decade his enthusiasm never wavered, no matter how many pages he read or how many times I bothered him with random trivia about war junk.At Western, I am also indebted to
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 | 0.002 | 0.009 |
| 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 | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.318 | 0.265 |
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".