The Bottom Billion: Why the Poorest Countries Are Failing and What Can Be Done about It: Some Insights for the Pacific?
Bibliographic record
Abstract
A noted scholar of fragile states and of African economies, Paul Collier, argues that the appropriate focus for today's development effort is those countries whose residents have experienced little, if any, income growth over the 1980s and 1990s. On his reckoning, there are just under 60 such economies, home to almost 1 billion people. Collier argues the plight of the 'bottom billion' is that they are caught in one (or often several) of four traps; (i) conflict; (ii) mismanaged dependency on natural resources; (iii) weak governance in a small country; and (iv) economic isolation among other very poor economies, with access to big markets available only at high cost. Or as he puts it in the African context, 'landlocked with bad neighbours'. Countries such as East Timor, Papua New Guinea and Solomon Islands suffer several of the four traps Collier identifies. The growth performance over the last quarter-century of the six Pacific economies in the bottom billion has been significantly weaker than the average of the other states in the bottom billion. Effectively aiding the Pacific's attempts to improve decades of very weak per capita income growth may benefit from the insights into novel and 'whole of government' forms of development assistance that Collier identifies for the 'bottom billion'.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".