Typology Construction for Comparative Country Case Study Analysis of Patterns of Growth in Sub-Saharan Africa
Bibliographic record
Abstract
Abstract This study has been motivated by the limitations of cross-country regressions and unstructured comparative case studies in providing policy-relevant findings on the determinants of patterns of growth. It presents a methodology to improve upon existing comparative case study research by situating cases withing a typological framework and subsequently using cluster analysis to improve the matching of cases with respect to a number of ‘weakly exogenous’ variables. Such an approach performs a taxonomic function, distinguishing different types of cases and an explanatory function by facilitating the comparison of similar cases in terms of variables in the typology (‘like with like’ comparisons) or of cases with one or more known differences with respect to these variables. The approach is illustrated using data on poverty and growth in SSA and uncovers a number of good comparator cases situated within a typological framework for subsequent comparative analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".