Research for understanding and promoting innovation by small-and-medium sized firms in the least developed countries
Bibliographic record
Abstract
With a strong policy focus on small and medium-sized enterprises (SMEs) in the least developed countries (LDCs), there is a demand for information that can inform efforts to promote innovation, technological development and SME development. This paper presents results from a bibliometric analysis of publications on SME development in the LDCs. It was informed by different perspectives on how SME innovation occurs: mainstream emphasis on formal research and development and a framework emphasizing learning and organizational capabilities. The results show that there are low levels of research on SME development in the LDCs and generally there is limited coverage on topics that can inform SME innovation and technological development in SMEs. Our findings highlight the need for further research on SME development in LDCs that is informed by a wide perspective of what innovation and technological development are in these countries and can guide policy efforts.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".