Bibliographic record
Abstract
The path to this volume has occupied nearly the full duration of the centennial of the Great War. The three collaborators and coeditors (who are still friends, amazingly) began by organizing a pair of international conferences: Over Here and Over There (University of York, England, February 27–28, 2015); and 1915: Music, Memory, and the Great War (University of Illinois, March 10–11, 2015). The first of these, conducted in tandem with an undergraduate module taught by William Brooks, included numerous performances, presentations, and exhibits by students and scholars, including Gayle Magee, Christina Bashford, and Deniz Ertan, each of whom has contributed to the present volume. The second conference included papers by many of the other authors represented here, with yet others in attendance; it included a performance by a Canadian troupe that re-created an entertainment given by Canada’s legendary “Dumbells” at the western front during the war and a recital by tenor Justin Vickers and pianist Geoffrey Duce, who presented multiple settings by English and American composers of the iconic text “In Flanders Fields.”...
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 0.028 |
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".