“I’m Sorry, but That’s Bribery”: A Decolonial Perspective From Which to Study Moral Economies in the ‘Chinese Pacific’
Bibliographic record
Abstract
“I’m sorry, but that’s bribery,” said reporter Tom Steinfort to Vanuatu Minister of Foreign Affairs Ralph John Regenvanu regarding the supposed support of the island state to China in United Nations resolutions, hypothetically framed as reciprocation for an unprecedented influx of foreign capital. This conceptualization of bribery rests upon recent value negotiations concerning the moral economy of corruption within the context of the ‘China threat’ debate in Oceania. A decolonial methodology is necessary to prevent this superimposition of colonial interests upon indigenous views in journalistic reports, social media outlets, and academic publications. It is, therefore, necessary to interrogate the position from which reporters, journalists, and scholars speak or write about corruption in diplomatic relations in an increasingly Sinicized Pacific. This approach appreciates localized forms of theorizing indigenous ideas about appropriate economic behaviors in the context of new geopolitical relations. In the absence of a decolonial methodology, such ideas might become invisible, along with the intrinsic features of new Sino-Pacific relations.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".