Bibliographic record
Abstract
This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. It is funded by the University of Leicester. Between 1415, when the Portuguese first used convicts for colonization purposes in the North African enclave of Ceuta, to the 1960s and the dissolution of Stalin’s gulags, global powers including the Spanish, Dutch, Portuguese, British, Russians, Chinese and Japanese transported millions of convicts to forts, penal settlements and penal colonies all over the world. A Global History of Convicts and Penal Colonies builds on specific regional archives and literatures to write the first global history of penal transportation. The essays explore the idea of penal transportation as an engine of global change, in which political repression and forced labour combined to produce long-term impacts on economy, society and identity. They investigate the varied and interconnected routes convicts took to penal sites across the world, and the relationship of these convict flows to other forms of punishment, unfree labour, military service and indigenous incarceration. They also explore the lived worlds of convicts, including work, culture, religion and intimacy, and convict experience and agency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.880 | 0.848 |
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; the direct Gemma label and the distilled Codex classifier 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".