Surveys in Economic Growth: Theory and Empirics
Bibliographic record
Abstract
1. Economic Growth in Transition: Donald A. R. George (University of Edinburgh), Les Oxley (University of Canterbury, New Zealand) and Ken Carlaw (University of Canterbury, New Zealand). 2. Specifying Human Capital: Ludger Wossmann (Ifo Institute for Economic Research, Munich). 3. Cost and Income Based Measures of Human Capital: Trinh Le, John Gibson (University of Waikato, New Zealand) and Les Oxley (University of Canterbury, New Zealand). 4. What Have We Learnt From the Convergence Debate?: Nazrul Islam (Emory University). 5. How Large is International Trade's Effect on Economic Growth?: Joshua J. Lewer (West Texas, A & M University) and Hendrik Van den Berg (University of Nebraska). 6. Fiscal Policy and Economic Growth: Martin Zagler (Vienna University of Economics & Business Administration and Free University of Bozen, Bolzano) and Georg Durnecker (Vienna University of Economics & Business Administration). 7. Growth and Unemployment: Towards a Theoretical Integration: Fabio Arico (University of Pavia). 8. Productivity, Technology and Economic Growth: What is the Relationship?: Kenneth I Carlaw (University of Canterbury, New Zealand) and Richard G. Lipsey (Simon Fraser University, Canada). 9. The Long--Run Implications of Growth Theories: Jonathan Temple (University of Bristol).
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 | 0.019 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.030 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".