Bibliographic record
Abstract
It seems strange to begin a list of acknowledgments with people who cannot be named, but it is my "informants" who made this book possible.To everyone who let me into their homes and lives, patiently answered questions, and helped me find other people to pester: you all have my deepest gratitude, not only for the information but for the chance to spend time as one should in Newfoundland, that is, in the kitchen telling stories.I was lucky to have as my companion on many of these occasions Martin Lovelace, who has been unflagging in support of this projectand twenty-plus years is a long time not to flag.During those early field trips he was busy tending to small John Rieti-Lovelace; now John is a journalist and editor whose energy was an inspiration to take the manuscript off the shelf.During its long incubation, portions of it benefited greatly from being read by Marianne Stopp and Philip Hiscock.I have had helpful discussions with almost all my friends but must particularly mention Janet McNaughton, Diane Tye, and Pauline Greenhill.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.303 | 0.218 |
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".