Bibliographic record
Abstract
Fragile states pose major development and security challenges. Considerable international resources are therefore devoted to state-building and institutional strengthening in fragile states, with generally mixed results. This volume explores how unpacking the concept of fragility and studying its dimensions and forms can help to build policy-relevant understandings of how states become more resilient and the role of aid therein. It highlights the particular challenges for donors in dealing with ‘chronically’ (as opposed to ‘temporarily’) fragile states and those with weak legitimacy, as well as how unpacking fragility can provide traction on how to take ‘local context’ into account. Three chapters present new analysis from innovative initiatives to study fragility and fragile state transitions in cross-national perspective. Four chapters offer new focused analysis of selected countries, drawing on comparative methods and spotlighting the role of aid versus historical, institutional and other factors. It has become a truism that one-size-fits-all policies do not work in development, whether in fragile or non-fragile states. This is should not be confused with a broader rejection of ‘off-the-rack’ policy models that can then be further adjusted in particular situations. Systematic thinking about varieties of fragility helps us to develop this range, drawing lessons – appropriately – from past experience. This book was originally published as a special issue of Third World Quarterly, and is available online as an Open Access monograph at https://www.taylorfrancis.com/books/e/9781351630337.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".