Bibliographic record
Abstract
These two books, with their very different approaches to what might seem to be much the same topic, complement rather than compete with each other. Both authors make abundantly clear the sheer awfulness of the conditions under which the First World War in the Middle East was fought, and the inexcusable waste of human life that resulted from the incompetence of the military command on all sides. The inadequacies of the Mesopotamia campaign have been known since the publication of the report of the HM Government's Mesopotamia Commission as early as 1917, but both authors' accounts of Gallipoli and the Dardanelles suggest equal measures of callous indifference and lack of preparedness on this front as well. Much the same can be said of some of the more disastrous Ottoman military operations, particularly the sheer insanity of the Caucasus/Sarıkamış campaign of late 1914 and early 1915. Here, Enver Pasha apparently thought it was worth sending tens of thousands of ill-clad and ill-equipped soldiers across passes over 2,000 metres above sea level in late December, in order to capture a strategic railhead in the Russian Caucasus and inflict a heavy defeat on the Russian Army. A campaign that lasted a mere two weeks caused the death of some 50,000 Ottoman soldiers, about a quarter of them from frostbite and exposure.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".