Small Business Exporters: A Canadian Profile
Bibliographic record
Abstract
Provides a statistical profile of Canadian smallbusiness exporters for the years 2000-2002, with data drawn from theExporter Registry and Statistics Canada’s Business Register. Thereport establishes new baseline information in order toprovide a basisfor policy development to foster small business exports. The principal findingis that, although small businesses individually export much less than largefirms, because of the large number of SMEs the value of their exports intotalis significant at 20 percent. Presents data on the number of exporters and impact of small exporterscompared with large ones. Of 35,594 exporters, 84 percent were smallbusinesses. The majority (78 percent) exported less from $1 million (Canadian$) annually. Also examines small business exporters by annual export value andanalyses their contribution to the overall value of exports.In 2002the total value of exports was $343 billion, of which small businessescontributed 20 percent. Data is also presentedon thevalue of exports by province,industry, and export destination. The largest exporters were Ontario andmanufacturing; the major destination was the United States. A comparisonof exports in Canada and the U.S. is given; Canadian and U.S. exportpatternsare very similar, except that Canadian exporters are more activeinternationally. Observations on the profiles and impact of small business exporters aregiven, as are barriers and economies of scale that mayhinder smallbusiness exporters. Suggests public policy be tailored towards the special needof small firms in order toincrease their export activity. (TNM)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".