Bibliographic record
Abstract
With this first issue of Volume 44, I have some announcements to make. First, I am very sorry to report the death earlier this year of one of our long-standing members of the Editorial Board, Vicki L. Eaklor. Vicki was a professor of US history at Alfred University for 30 years before retiring in 2014. She served as a guest editor, peer reviewer, and contributor to the journal for most of her tenure at AU. A terrific colleague, talented musician, creative scholar, and brilliant teacher, Vicki will be missed by all who knew her zest for life, intellectual rigor, sense of humor, and love of good bourbon. Second, as of this issue, I am announcing my retirement from the position of senior editor of Historical Reflections/Réflexions Historiques. Third, I have the distinct pleasure of announcing that one of our Editorial Board members, Dr. Elizabeth Macknight, of the University of Aberdeen, Scotland, will be taking on the duties of senior editor beginning with the second issue of the year, and that W. Brian Newsome will be remaining as coeditor.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 0.065 |
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".