Bibliographic record
Abstract
Abstract When are developing countries able to initiate periods of rapid growth and why have so few of these countries been able to sustain growth over decades? Deals and Development: The Political Dynamics of Growth Episodes seeks to answer these questions and many more through a novel conceptual framework built from a political economy of business–government relations. Economic growth for most developing countries is not a linear process. Growth instead proceeds in booms and busts, yet most frameworks for thinking about economic growth are built on the faulty assumption that a country’s economic performance is largely stable. Deals and Development explains how growth episodes emerge and when growth, once ignited, is maintained for a sustained period. It applies its new framework to examining the growth of countries across a range of institutional and political contexts in Africa and Asia, using the examples of Bangladesh, Cambodia, India, Malaysia, Thailand, Ghana, Liberia, Malawi, Rwanda, and Uganda. Through these country analyses it demonstrates the explanatory power of its framework and the importance of feedback cycles in which economic trends interact with political behaviour to either sustain or terminate a growth episode. Offering a lens through which to analyse complex scenarios and unwieldy amounts of information, this book provides actionable levers of intervention to bring around reform and improve a country’s chance at achieving transformative economic growth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".