Bibliographic record
Abstract
I have selected cultural and business specialities in Canada as a objective in my work. In these days it is really necessary to know witch cultural and business dissimilarities can we meet during business negotiation. It is my hope that my work will provide aggregate information about Canadian culture and will attract more interested person. For instance: business representatives, when they want to know about the country they want to do business with, correspondence departments they have to maintain the relationship with Canadian business partners, students they want to go to Canada for holiday or at last tourists they are going to visit the country of maple leaf on their journey. In the prologue I have tried to set the goal and the structure of my work. In first and second chapter I pay attention for territorial characteristics, economic situation and foreign trade. I also have taken down a legal framework of Canada. I found out that there is huge difference among Canadian provinces. Where some business tactics will be kindly accepted as a professional, in other province can this behaviour be taken as a lack of interest or dispassionate. Third chapter provides intercultural characteristics. On models of two well known anthropologists I have tried to explain Canadian behaviour- their ways of thinking, ways of acting and why are they different from their neighbours from US. Fourth and fifth chapter deals with cultural and business dissimilarities in Canada. I have explained why is Canadian culture of business so special from other cultures. Language issue, social values or inclination to recycle and other problematic has been mentioned. The conclusion of my work has shown if I succeeded in my goals that I set in prologue. I am aware, that this topic is so extensive that I was not able to cover all of it but I hope that I have provided many useful information and knowledge about this so various region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.022 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".