Hayek-Myrdal Interactions in the Early 1930s: New Facts Change an Old Story
Bibliographic record
Abstract
Abstract It is widely believed that Friedrich Hayek's first encounter with Gunnar Myrdal involved the latter's last-minute contribution, as a replacement for Erik Lindahl, to a Sammelband edited by the former in 1933, and that Hayek was lukewarm toward Myrdal and his ideas from the very beginning. Correspondence between the two shows that, in fact, their interaction about this contribution began two years earlier and that their relationship was cooperative and cordial prior to its publication. We suggest that it was the content, and in particular the tone, of Myrdal's 1933 paper, originally intended for the Journal of Political Economy, that alienated Hayek, who nevertheless treated Myrdal's work with academic propriety thereafter. In the course of our discussion, new light is also thrown on the origins of Myrdal's famous ex post–ex ante terminology.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".