Moving From ‘Developmental’ to ‘Anti-Developmental’ Local Financial Models in East Asia: Abandoning a Winning Formula
Bibliographic record
Abstract
One of the decisive but often overlooked factors in the creation of the East Asian ‘economic miracle’ was the part played by a variety of heterodox sub-national state, community and cooperatively owned and controlled financial systems, institutions and lending models. Beginning with Japan after 1945, local financial systems were (re)constructed across East Asia in a way that very efficiently operationalised key development policy goals through targeted local enterprise development. Yet in spite of marked success with this ’developmental’ local financial model, from the 1980s onwards the international development community, led by the US government and the World Bank, began an effort to discredit and replace it with a new commercially-oriented private sector- led local financial model promoting mass individual entrepreneurship with the help of a for-profit microcredit sector. This article begins by briefly summarising why such ‘developmental’ local financial models were important to East Asia’s economic miracle before I turn to examining why, how and what happened when after 1980 the international development community quietly set out to undermine and destroy them. I conclude from this analysis that the international development community’s desire to begin to impose its own neoliberal ide- ology and narrow elite-driven enrichment goals in East Asia far outweighed the ongoing development successes registered by the ‘developmental’ local financial models that emerged after 1945.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".