EXPORT CHARACTERISTICS OF CANADIAN FIRMS IN THE COMMERCIAL GEOGRAPHIC INFORMATION SYSTEMS (GIS) INDUSTRY
Bibliographic record
Abstract
This paper examines the export characteristics of Canadian firms in the commercial geographic information systems (GIS) industry. Evidence from a sample of 351 Canadian GIS companies suggests that export success correlates positively with a wide range of organizational variables, including R&D spending, external collaboration (alliances with complementary firms), recourse to government support systems, and occupational structure (in-house technical skills). A notable finding is that company size plays no discernible role in export performance. If anything, very small firms exhibit better export performance than their larger counterparts. A further finding is that some of the strongest predictors of export involvement pertain to the extent to which GIS firms exploit government programs and/or other public initiatives in areas that relate to foreign sales development. A regression analysis based on predictor variables from a two-group discriminant model (exporters versus non-exporters) reveals that export success is strongly influenced by in-house research effort, foreign travel, external collaboration, and product customization. The paper concludes with a brief discussion of the implications of the empirical results for policy-oriented research on export promotion.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".