Bibliographic record
Abstract
The article Are There Laws of Production? published in the American Economic Review in 1949 roused a great deal of interest among specialists, it has often been quoted and reprinted on several occasions. In the article Paul H. Douglas presented the results of his many years of studies. Having processed a great deal of statistical data, using the production function suggested by him together with C.W. Cobb, Douglas attempted to determine the share of labour and capital in the final product of the manufacturing industry in a number of countries and regions. The results were as follows: there was a surprising constancy in the share of labour and capital within individual countries throughout the research period and the returns from additional inputs of labour and capital were practically constant. For the US, Australia and South Africa the share of labour was close to 2/3 and the share of capital was 1/3. For New Zealand and Canada the share of labour was lower and capital higher, but the shares remained stable throughout the entire period of observation. The author suggested that results such as these could not be random and there was clearly a law of production, which may explain the current shares of labour and capital in a manufactured product. If we take into consideration the well-known fact that the share of consumption in the GDP is very close to 2/3, the conclusions drawn by Paul H. Douglas seem entirely reasonable and require a certain kind of explanation. Let us try to analyse the results obtained and respond to the question, which is as of yet unanswered: Are there laws of production?
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".