Bibliographic record
Abstract
The five British and Canadian generals depicted in Corps Commanders were a surprisingly eclectic lot – one a consummate actor, one a quiet gentleman, one a master bureaucrat, one a brainy sort with little will, and the last a brain with will to spare. And yet they all fit readily into British Commonwealth armies and fought their corps in similar fashion. All three Canadians controlled British formations and served under British army commanders, and the two Britons worked for and led Canadians as well. Such inter-army adjustments were relatively simple because they all spoke the same “language” – a common method for solving military problems and communicating solutions. Like all senior commanders in the British Commonwealth, they learned the language of the staff colleges at Camberley and Quetta, and so did the staff officers that served them. This allowed a gunner from Montreal to understand a guardsman from London with ease – no small advantage when coordinating coalition battles involving tens of thousands of troops. In probing how these corps commanders fought, Douglas E. Delaney has produced an invaluable study for anyone interested in coalition warfare, interoperability, or how men managed large formations in war.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.224 | 0.081 |
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".