Bibliographic record
Abstract
It is a matter of fact that the size of the Royal Navy has contracted significantly since the end of the Second World War. The stark reality of this reduction in numbers is amply demonstrated by noting that in terms of destroyers and frigates, the workhorses of any modern fleet, the Royal Navy possessed 19 in 2010; in 1950 it had 280. A simple decline in numbers is not the entire picture however. The reduction in fleet size is linked to a variety of factors including technology, cost, shifts in global politics and the divesting of formal empire. Furthermore, every navy in the West has seen a general contraction; even the United States Navy (USN) is facing reductions to overall numbers. So, where does the Royal Navy sit amid the hierarchy of the world’s navies today following its fall from dominance? Counting hulls is a crude approach but it does at least provide some means of comparison. If all types of submarines are excluded, as the focus of this study is the surface fleet, the Royal Navy has 57 warships of all types. This compares with China 348, Russia 172, India 130, Japan 88, Brazil 68, France 65, Australia 32 and Canada 22. 1 In crude numbers, this places the Royal Navy seventh in rank among these navies. These 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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.173 | 0.027 |
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".