Bibliographic record
Abstract
Books reviewed in this article: Meredith Fletcher, Digging People up for Coal: A History of Yallourn David Goldsworthy, Losing the Blanket: Australia and the End of Britain's Empire B. W. Higman, Domestic Service in Australia John Singleton and Paul L. Robertson, Economic Relations between Britain & Australasia 1945–1970 Peter McMahon, Global control information technology and globalization since 1845 Anthony B. Dickinson and Chesley W. Sangar, Norwegian Whaling in Newfoundland: The Aquaforte Station and the Ellefsen family, 1902–1908 Poul Holm, Tim D. Smith and David J. Starkey (eds.), The Exploited Seas: New Directions for Maritime Environmental History Eric Jones, The Record of Global Economic Development John E. King, A History of Post Keynesian Economics since 1936 Kunibert Raffer and Hans Singer, The Economic North‐South Divide: Six Decades of Unequal Development Scott Adams, Please Don’t Feed the Egos & Other Tips for Corporate Survival Scott Adams, The Dilbert Bunch José Miguel Martínez Carrión (ed.), El Nivel de Vida en la España Rural, Siglos XVIII‐XX Michael Latzer and Stefan W. Schmitz (eds.), Carl Menger and the Evolution of Payments Systems: From Barter to Electronic Money Howard W. Dick, Surabaya, City of Work: A Socioeconomic History, 1900–2000
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.626 | 0.634 |
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".