Bibliographic record
Abstract
Using annual data from 1961 to 2005 growth rate of gross domestic product at the constant prices of year 2000 is taken in the dependent variable and growth rates of employment level, gross fixed capital formation and lag\ndependent variable are all the explanatory variables, we obtained total factor productivity by using Cobb Douglas Model. The corresponding time period’s data of three happiness indices – life satisfaction, ecological\nfootprint and life expectancy is taken to determine the effect of happiness indices on total factor productivity. Negative impact of ecological footprint index on TFP is found in Canada, Japan, Norway, Spain, and UK, but is\nfound significant in the cases of Canada, Norway, Spain and UK. Life expectancy is found to be significantly explaining TFP in Netherlands, Norway, Spain, UK and USA. As far as the subjective index of happiness – Life Satisfaction – is concerned the slope coefficient is insignificant in all the\ncases except the USA. Estimates from pooled regression show that growth rates of ecological footprint index and life expectancy both are significantly explaining TFP, but life satisfaction index is found to be insignificant. Endorsing Loria’s viewpoint there is not only a need to check national\nincome accounts but there is also a need to develop happier societies. Enhancing happiness – the intangible capital – could be helpful in explaining total factor productivity in the neoclassical growth model.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".