Bibliographic record
Abstract
The Canadian domestic market is limited, andsoCanada relies heavily upon the exporting of goods to maintainemployment levels and to increase economic growth.To encourage firms toexpand globally, the Canadian federal government established Team Canada Inc.(TCI), a system of government agencies who provide support to exporters. A summary of several Canadian government policies that were created topromote exporting is provided.Five major Canadian programs are described:theforum for international trade training (FITT), New Exporters to BorderStates (NEBS), Exporters to the United States (EXTUS), Reverse NEBS, and NewExporters Training and Counselling Program (NEXPRO).A detailed overviewof each program and its objectives is included.Although the programs havedifferent strategies regarding the education and training of entrepreneursinterested in internationalizing their businesses, each respective program'sobjective focuses on the importance of exporting among Canadian industries. In closing, the challenges of the programs are considered, including thelimitations of addressing the personality traits that often characterize thosewith entrepreneurial aspirations.Other limitations are discussed, as arethe means by which the agencies have addressed the concerns. (AKP)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".