A Non-parametric Analysis of Convergence in ICT Industries
Bibliographic record
Abstract
The purpose of this article is to explore the relative merits of capital accumulation and efficiency catch-up in the convergence patterns of labor productivity in theICT (Information and Communication Technologies) sector in a set of developed countries. It is the first convergence analysis of ICT carried out from the non-parametric kernel approach and using an intertemporal data envelopment analysis (DEA) to estimate the efficiency of the analyzed countries. Special care has been paid to the dataset construction, using hedonic prices and unit value ratios because of the nature of the industry. The appropriate technology theory extended with non-immediate spillovers is the theoretical framework used to interpret the obtained results. These show thatlabor productivity, technology and efficiency have moved from a unimodal towards a bimodal distribution over time, beginning the 21th century with two convergence clubs of countries. The conclusions obtained from these results show that while capital intensification offer opportunities to benefit from new knowledge developed by the leaders, assimilation of this knowledge is not immediate and its speed depends upon the social and technological capabilities of the followers. Policy decision-makers should be aware that the choice of the technology has to be complemented with the development of other actives to benefit from all its potentialities.
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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".