Innovative Capabilities as Determinants of Export Performance and Behaviour: A Longitudinal Study of Manufacturing SMEs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Even though small and medium-sized enterprises’ (SMEs) share of world trade still remains much lower than that of larger firms, numerous studies indicate that many SMEs are nevertheless very active abroad, and rely increasingly on the development of foreign markets to ensure corporate growth. For example, SMEs are ‘directly producing about 20 percent of OECD exports and about 35 per cent of Asia’s exports’ (OECD, 1997, p. 7). A report issued by the US Secretary of Trade and Commerce reveals that 70 per cent of all exporting firms were small firms with fewer than 100 employees (Prozak, 1993). SMEs are also the fastest-growing group of exporters in the USA (Axinn et ah, 1994, p. 49). A similar trend is observed in Canada, where the number of SMEs involved in export activities doubled in the six-year period from 1986 to 1992 (Industry Canada, 1996). In the future, SMEs are likely to be even more exposed to international competition (Reynold, 1997; OECD, 1997).
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it