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Record W2889827801 · doi:10.1162/rest_a_00161

Ruggedness: The Blessing of Bad Geography in Africa

2009· preprint· en· W2889827801 on OpenAlex
Nathan Nunn, Diego Puga

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Review of Economics and Statistics · 2009
Typepreprint
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsnot available
FundersComunidad de MadridSocial Sciences and Humanities Research Council of CanadaCanadian Institute for Advanced Research
KeywordsTerrainBlessingGeographyDevelopment economicsEconomic geographyEconomicsCartographyArchaeology

Abstract

fetched live from OpenAlex

There is controversy about whether geography matters mainly because of its contemporaneous impact on economic outcomes or because of its interaction with historical events. Looking at terrain ruggedness, we are able to estimate the importance of these two channels. Because rugged terrain hinders trade and most productive activities, it has a negative direct effect on income. However, in Africa rugged terrain afforded protection to those being raided during the slave trades. Since the slave trades retarded subsequent economic development, in Africa ruggedness has also had a historical indirect positive effect on income. Studying all countries worldwide, we find that both effects are significant statistically and that for Africa the indirect positive effect dominates the direct negative effect. Looking within Africa, we also provide evidence that the indirect effect operates through the slave trades.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.495
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.304
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it