Africa and the Multilateral Trading Regime: Re-examining the “Market Access” Mantra
Bibliographic record
Abstract
Much has been written about sub-Saharan Africa and its inability to achieve economic development – a state of affairs often articulated by legal scholars in terms of its inability to realize the core elements of economic and social rights such as healthcare, food, housing, and basic education. Commentators and policy-makers are almost unanimous in blaming factors such as “inadequate” foreign aid, the debt burden, “unfair” multilateral trade rules and protectionism in Western markets. Although some are beginning to challenge the aid/debt hypothesis, the last two suppositions remain an article of faith – the overwhelming opinion being that existing multilateral trade rules and protectionism in Western markets represent the impediment to the region’s ability to “trade its way out of poverty.” This paper aims to add to the dissenting voices by arguing that the problem of protectionism has become so overemphasized that its proponents have, quite paradoxically, become the obstacle to the region’s prospects of ever achieving the aim of economic development as proclaimed under the Agreement Establishing the World Trade Organization and indeed, under the main international human rights instruments such as the Covenant on Economic and Cultural Rights – not least because this thesis invariably deflects critical attention from the real impediments to the region’s ability to trade.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".