Small business experience between Canada and Mexico
Bibliographic record
Abstract
THIS RESEARCH STUDY EXAMINES THE RELATIONSHIPS BETWEEN SMALL CANADIAN FIRMS AND MEXICAN COMPANIES WITH A PARTICULAR INTEREST IN THE COMPETENCIES AND PERSONAL EXPERIENCE OF ENTREPRENEURS DOING BUSINESS WITH MEXICO. INDEPTH INTERVIEWS WERE CONDUCTED WITH FOURTEEN CANADIAN BUSINESSMEN IN BRITISH COLUMBIA, A WESTERN PROVINCE OF CANADA. QUALITATIVE ANALYSIS OF THE INTERVIEWS SUGGESTS THAT ENVIRONMENTAL SCANNING IS AN IMPORTANT COMPETENCY FOR STARTING INTERNATIONALIZATION INITIATIVES, AS WELL AS HAVING A CLEAR VISION OF THE GOALS AND STRENGTHS OF THE BUSINESS. THE LEADERSHIP STYLE OF THESE MANAGERS IS GENERALLY INSPIRATIONAL AND VISIONARY, AS SUGGESTED BY BASS (1999). FURTHER, THE CULTURAL SENSITIVITY OF CANADIAN ENTREPRENEURS IS FAVORABLE TOWARD MEXICAN BUSINESSPERSONS. SUCCESSFUL BUSINESS RELATIONSHIPS ARE BASED ON DEVELOPING TRADING PARTNERS, AND WE IDENTIFY BEHAVIORS FROM MEXICAN COMPANIES THAT ARE VIEWED AS FACILITATING OR HINDERING BUILDING BUSINESS RELATIONSHIPS.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".