A Challenge to the Discourse of Development or Development Done Differently: The Discourse of Experts in the WTO Agreement on Trade Facilitation
Bibliographic record
Abstract
The 2017 coming into force of the WTO Agreement of Trade Facilitation, and its special and differential treatment provisions for developing and least developed countries, is expected to leverage substantial international development resources, along with the dominant discourse of development, including the deployment of Western-trained experts to support the implementation of complex border management measures on the basis of a neoliberal discourse of good governance.Critical development scholarship, informed by Escobar (2011), Ferguson (1994) and Li (2007) helps to inform the texture of expert-beneficiary relations, while a poststructuralist discourse analysis helps to reveal the underlying power relationships as reflected in texts and practices.This study will explore these dominant discourses, paying particular attention to the peer-to-peer expert deployment mechanism employed by the Brussels-based World Customs Organization and the case of Sierra Leone, which offers potential to challenge the dominant tactics employed by international development agencies.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.050 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| 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".