Engaging SMEs in Cooperation and New Forms of Learning
Bibliographic record
Abstract
Globalization has strong if contradictory impacts on SMEs. The sharp increase of worldwide competition in recent decades weakened SMEs right across the board. At the same time, globalization offers many new opportunities and the importance of SMEs has increased. Opportunties include the potential to act as collaborators in multi-enterprise consortia, for international clients and partners. Their importance has increased because of their specific potential, such as high specialization and the ability to react faster than big enterprises. Therefore, it is important for such companies to take up the challenge to operate on a global scale. To be able to play this role, SMEs have to tackle change and develop their learning processes on a far-reaching scale. If the triangle and interplay of continuous learning, knowledge management and new technologies, is key in making or breaking companies as well as national and supranational entities (such as the EU), it is important new means of addressing these areas, particularly through the use of ICT are explored. While ICT and especially web 2.0 applications – social networks, blogging, web repositories and shared resources, modern learning platforms – have great potential to support and enable SMEs in their drive towards competitiveness, the realization of this potential is by no means automatic. Success depends on a strategic approach, technical skills and facilities. This paper takes a closer look at the situation of European SMEs and presents findings from recent European projects identifying the ongoing problems in the adoption of new forms of learning. Secondly the paper discusses learning approaches and web-based technologies particularly suitable for SMEs.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".