The Economist Critique of the Romano-Germanic Tradition
Bibliographic record
Abstract
The economist (in the ideological sense) analytical grid adopted in the World Bank's Doing Business reports is often represented as intrinsically hostile to the civil law tradition. If, indeed, some presuppositions of these reports are problematic from the standpoint of a fair appraisal of the performance of states sharing that legal tradition, a deeper examination of the theoretical frameworks that have influenced the intellectual model adopted in these reports reveals a more complex reality: the link between the adoption of an economist analytical grid and the negative criticism of the civil law tradition is contingent instead of being inevitable. This paper examines the frameworks (neo-institutionalist economics, normative approaches to law and economics, legal neo-libertarianism) which have directly or indirectly exerted some influence of the evaluative model adopted in the Doing Business reports. It sheds light on the exceptionalism, within economic theories, of the Legal Origins movement - the main source of inspiration of the reports - when it comes to grasping and interpreting the correlations between a state's belonging to a particular legal tradition and that state's performance. It finally draws attention to the epistemic and strategic interest that civilian jurists would find in opening themselves, more than they currently do, to the insights of other bodies of knowledge and to their conceptualization of legal institutions and phenomena.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".