Bibliographic record
Abstract
Francesco Giavazzi of Bocconi University reviews “Institutions and Economic Performance” by Elhanan Helpman,. The EconLit Abstract of the reviewed work begins “Thirteen papers examine why income per capita varies so greatly across countries and the role of institutional differences. Papers discuss the impact of administrative power on political and economic developments--the move toward a political economy of implementation; the institutional origins of the Industrial Revolution; institutions and the resource curse in early modern Spain; slavery, inequality, and economic development in the Americas--an examination of the Engerman-Sokoloff hypothesis; economic and political inequality in development--the case of Cundinamarca, Colombia; the constitutional choice of bicameralism; economic development, insurgency, and civil war; party discipline and pork-barrel politics; policy persistence in multiparty parliamentary democracies; formalizing informal institutions--theory and evidence from a Kenyan slum; making autocracy work; democracy, technology, and growth; and the growth effect of democracy--whether it is heterogeneous and how it can be estimated. Helpman is Galen L. Stone Professor of International Trade at Harvard University and Fellow of the Canadian Institute for Advanced Research. Name and subject indexes.”
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.333 | 0.331 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".