Bibliographic record
Abstract
When war broke out in 1914, the protagonists' empires automatically joined. Britain's imperial contribution varied between the 'white' (Australia, Canada, Newfoundland, New Zealand and South Africa), 'brown' (India) and 'black' (Africa/West Indies) dominions/ colonies. While 'white', 'brown' and 'black' dominions all provided combat troops, black African soldiers (some 56,000) were usually deployed outside Europe. Britain, however, employed black Africans in labour units in Europe. Excepting India, in 1914 Britain's imperial territories had tiny armies supported by part-time militias. Once war started, the white dominions had to create expeditionary forces from scratch. Meanwhile, France recruited indigenous soldiers from her empire in Africa and Indo-China for the war fronts and for labour duties behind the lines. Belgium and Portugal also tapped the resources of their empires, as did Russia, whose land-based empire stretched into the Caucasus, Central Asia and Siberia.KeywordsWestern FrontForeign LegionOttoman EmpireWhite DominionLeeward IslandThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".