The competitiveness of SMEs: obstacles and the need for outside help
Bibliographic record
Abstract
In this communication, we present the results of an extensive survey of more than 1000 SMEs in the Canadian province of Quebec in order to identify the principal obstacles likely to have a negative impact on their competitiveness over the next two years. For the particularly challenging obstacles, the enterprises were also questioned on their need for outside help and, if so, the type of aid hoped for. The results show that the SMEs indeed do need outside help, and the types of aid most often mentioned are financial help from the government, tax credits, and training. Moreover, the obstacles to competitiveness that confront the SMEs as well as the kinds of external assistance hoped for vary in accordance with the various dimensions that characterize their heterogeneity, in particular the size of the enterprises and their location, i.e., if they are situated in urban, central, or outlying areas. These conclusions confirm that government intervention is hoped for in order to permit SMEs to take up the challenges of globalization, but that this help must be tempered to take into account the particularities of 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".