Conclusion: the business of economic policy-making, comparatively speaking
Bibliographic record
Abstract
An ambitious shopkeeper class, urban homogenization … and intense minority group problems on the one side give a quite different picture than … a fusion of political and economic elites … and the confinement of business activities to a small percentage of the population give on the other. In a classic study, Clifford Geertz reflects on the differences between two very different Indonesian towns, one dominated by peddlers, the other by princes. Modjokuto, on the one hand, is a market-centered society with an extraordinarily energetic commercial life characterized by “vigorous competitive interaction.” The Muslim traders are somewhat marginal to the political life of the town but their industry forms the dynamic core of its economic life. By contrast, in Tabanan, “it has not been the bazaar but the palace which has stamped its character upon the town.” Here the political elites are dominant, and they would like to parlay their political power into economic power too. The danger is that “Tabanan's firms can become easily politicized in modern terms and this is … extremely dysfunctional to further growth or even to continued solvency.” The same phenotypes are evident in the griot's account of two sons of a Malian king: “the younger preferred fortune and wealth and became the ancestor of those who go from country to country seeking their fortune” while “the elder chose royal power and reigned.”
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".