Bibliographic record
Abstract
Given the financial challenges students face in higher education, my hope with this textbook is to draw on my 20+ years of entrepreneurship experience and academic research to compile resources that reflect the general aspects of starting a business. Business Startup and Entrepreneurship: Canada is an Open Educational Resource (OER) textbook that focuses on the practical, current, and relevant topics associated with starting a business in Canada. This broad practical approach allows instructors to introduce these simple chapters to build a basic understating of the essentials to startups. Along with this textbook is an accompanying Business Plan Workbook, helping students to work through a business idea chapter-by-chapter that hopefully culminates in a well thought-through startup plan. In my experience teaching introductory entrepreneurship courses, developing a business plan supports deep-level learning that is displayed through critical analysis, creativity, and networking. The design of the business plan has been simplified and tested over the past three years with over 750 students from diverse cultures and academic disciplines. It is my recommendation that students work on the business plan throughout the duration of the course as they complete chapters to reaffirm important lessons. Students may find tasks such as finance and accounting difficult, which is why an Excel Worksheet accompanies the business plan to help prepare financial reports for their proposed businesses.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.014 |
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".