‘Parity with all nations’: The ‘coolie’ trade and the quest for recognition by China and Japan
Bibliographic record
Abstract
Abstract This article explores the quest for sovereign equality by China and Japan as it unfolded in a specific historical moment, the third quarter of the nineteenth century. It does so by focusing on the debate around the ‘coolie trade’, i.e., the traffic of Chinese indentured labourers, which offered an opportunity for non-Western countries such as China and Japan to position themselves with respect to Western conceptions of ‘modernity’ or ‘civilization’ and thereby advance their quest for ‘parity with all nations’. Through a study of the Maria Luz case, decided in the early 1870s by Czar Alexander II and drafted by de Martens, the article sheds light on the different approaches of Japan and China with respect to international law at this critical historical juncture. Specifically, it shows that, although the coolie trade mostly affected China, it was Japan who first managed to reap a parity dividend by firmly condemning the practice, whereas China’s action was steered by the circumstances. Eventually, however, China’s growing interest in Chinese populations abroad paved the way for the establishment of its first permanent diplomatic representations overseas. For both countries, the events encapsulated by the Maria Luz case unveil an important, yet overlooked, moment in their quest for parity with all nations and, more generally, in their engagement with international law.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".