Mark Lardas, South China Sea 1945: Task Force 38’s bold carrier rampage in Formosa, Luzon, and Indochina by Emily Golden
Bibliographic record
Abstract
Reviews 307 It produces a homogenized history and makes following up and fact-checking difficult.Use of a wider group of sources would give a less-biased result.All titles are shown as published in the UK.It excludes Cunningham's memoir, Sailor's Odyssey, while quoting from it in summing up the battle.Konstam includes three Norman Friedman titles, on radar, British cruisers, and big ship gunnery, but excluding his (much more germane) Naval Anti-Aircraft Guns and Gunnery (Seaforth, 2009).At thirty-three dollars Canadian, the book is pricey.The physical product of the paperback volume is close to a magazine format, with high gloss, kaolin paper in a rugged "perfect" binding, which makes it durable.The book is part of Osprey Publishing's integrated website that presents and markets the Campaign and other series.On the back cover there are three miniature photographs.The top one is of Cunningham.The accompanying text makes me wonder whether the person who wrote it had actually read the book: "world-leading maritime historian Angus Konstam tells the fascinating story of how Allied ships failed to repulse Axis convoys," when it is the story of how troop convoys successfully driven off during the period covered.The book would hold more general interest with more detail on the Regia Marina and Regia Aeronautica structure and well as RN Fleet Air Arm and RAF participation.Along with more information on Italian ships and aircraft would provide more balance as would graphics, like a cutaway diagram of a MAS Boat and torpedo boat as is done for Dido class and J/K/L destroyers.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".