Bibliographic record
Abstract
Abstract Immiserizing growth (IG) refers to situations where economic growth does not lead to poverty reduction. How should this phenomenon be conceptualized? How often, when and where does it occur? Why does it occur? This book aims to address these three sets of questions, drawing on a wide range of theoretical perspectives and empirical approaches. It presents a conceptualization of IG which combines the notions of failed and malevolent inclusion, being bypassed and ‘avoidably’ harmed by growth, respectively. It addresses the second set of issues drawing on comparable household survey data from around 1990 using multiple poverty lines and time periods, and different measures of growth and poverty. It reviews explanations of immiserizing growth found in a wide variety of bodies of thought including the classical tradition of political economy (Mathus, Ricardo, and Marx), radical traditions of scholarship, literatures on poverty dynamics and inclusive growth, and empirical case studies. Eight categories of processes and mechanisms of IG emerge from these literatures related to: (i) sectoral, spatial or other dimensions of growth; (ii) poverty traps; (iii) public action or inaction; (iv) changes in relative prices or the terms of trade; (v) technological change; (vi) violence and conflict; (vii) dispossessions and indebtedness and viii) environmental degradation and ‘natural’ phenomena. Part II of this book empirically investigates some of these potential drivers of IG using econometric analysis, Qualitative Comparative Analysis (QCA) and case studies. The various chapters of this book make distinct historical, theoretical, methodological, and empirical contributions and further our understanding of a phenomenon which remains underexamined and inadequately understood.
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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 | 0.006 |
| 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 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".