Bibliographic record
Abstract
In this paper, I ask the question: Does the output-mix of countries change in response to changes in factor endowments? If so: How long does it take? Using data on capital, as well as skilled and unskilled labour employed in three-digit International Standard Industrial Classification (ISIC) manufacturing industries for a sample of 27 developing and developed countries over the 1973–1990 period, I find that the output-mix of countries does not change in response to endowment changes, even after 15 years. This answer raises another question: How then do countries absorb changes in factor endowments? The data show that in both the short and long runs, an increase in the supply of a production factor reduces its rate of return and makes it more intensively used in all sectors of the economy: changes in production techniques. In the long run, the point estimate is that the reduction in the rate of return is more than 50% larger than in the short run. This is consistent with induced innovations being predominantly biased towards the scarce factor.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".