ICT, INNOVATION, WAGES AND LABOUR PRODUCTIVITY. NEW EVIDENCE FROM SMALL LOCAL FIRMS TIC, INNOVACIÓN, SALARIOS Y PRODUCTIVIDAD DEL TRABAJO. NUEVA EVIDENCIA PARA EMPRESAS PEQUEÑAS Y LOCALES
Bibliographic record
Abstract
This article analyses new co-innovative sources (ICTs, human capital and training, and new forms of work organisation) of labour productivity in small firms producing for local markets. Using 2009 survey data for a representative sample of 464 firms based in Girona (a province in the north-east of Spain) and using Ordinary Least Square (OLS) econometric estimation techniques, two main findings have emerged from the study. First, that mean wage is the main determinant of labour productivity. And second, unlike the evidence available for larger firms, co-innovation does not have a total effect on explaining small local firm's labour productivity. Causal relationships between co-innovation and labour productivity have only been identified in the innovative small local firms, one quarter of the sample.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".