Assessing European national policies to support the competitiveness of information and communication technology producers
Bibliographic record
Abstract
Purpose In the context of fears that the European information and communication technology (ICT) sector may be facing a period of crisis, this paper seeks to examine the changing role of national‐level policy initiatives to enhance the competitiveness of European ICT producers. Design/methodology/approach The article is based upon a study of 176 national programs that are aimed specifically or in substantial part at ICT producer goods. This supply‐side focus provides a counterpoint to studies that concentrate on demand stimulation and aggregation measures, which generally make up a much larger share of national policy programs. A comparative analytical framework is used that takes account of the different composition and structure of the ICT industries in the EU member states. Findings The key findings are that technology development programs continue to dominate but that the emphasis is shifting from ICT producer goods as such to the application and coordination of ICT products and services across a wide range of industry contexts. This process takes different directions depending upon national political and administrative structures and historical national attitudes to industry policy. Originality/value The article gives evidence about sector specific strategies for supporting the competitiveness of the ICT sector and forms the basis for the identification of best practice examples.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".