Reflections on the Value of Socio-Legal Approaches to International Economic Law in Africa
Bibliographic record
Abstract
In their Lead Essay for the 2021 Chicago Journal of International Law Symposium, Daniel Abebe, Adam Chilton, and Tom Ginsburg offer an account of “the rise of the social science approach to international law, explain the basics of the method, and advocate for its continued adoption.” This Essay critically assesses how and why one might use socio-legally inspired methods (analytical, empirical, and normative) for the study of international economic law (IEL) in Africa. It illustrates the empirical method’s importance in understanding one of the most challenging aspects of the study of IEL in Africa: capturing the data and dynamism of informal cross-border trade phenomenon. It argues that, by conceptualizing IEL in Africa as a social phenomenon, socio-legal approaches open IEL in Africa to the application of other social science methods, which enables us to understand the context in which African regional trade agreements are implemented and their contribution to the scholarly field of IEL
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.087 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.018 |
| 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".