Growth of ICT Capital and Deceleration of Labour Productivity in the EU Countries: The Missing Links
Bibliographic record
Abstract
Labour productivity in most of the EU countries grew much slower than in US over the last one and a half decades and the difference is attributed to the difference in the use of ICT. Analysing EU KLEMS database (capital (K), labour (L), energy (E), material (M) and service inputs (S)) and Eurostat database it is noted that the micro and small enterprises, numerically predominant in the EU countries, use much less amount of ICT. With very low proportion of enterprises with ICT installation, with less sophisticated technology and probably with the lowest amount of ICT capital, these enterprises employ relatively larger proportion of workers who use ICTs. The larger enterprises on the other hand with more sophisticated and larger quantity ICT capital employ fewer workers who handle this technology. An implication of this is the fast growth of productivity of selected highly ICT skilled workers of the larger enterprises leaving rest of the workforce to benefit least from the technology. It is obvious under this situation that the overall productivity growth of the workers would be stunted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 0.000 |
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".