CGA + MBA A powerful graduate and professional education partnership Laurentian University Online MBA
Bibliographic record
Abstract
Application PackageApplication Process Please read the following instructions very carefully before completing any of the attached forms, requesting references or writing your letter of application. Admission to the MBA program is competitive and will be based on the Laurentian University Admissions Committee relative rankings of applications. The Committee will review each application individually and assess each applicant’s portfolio of prior education, work experience and unique strengths in determining the applicant’s ability to successfully undertake the MBA program. Generally, the following admission requirements apply: • Completion of Levels 1-4 of the CGA program or equivalent • Minimum two years of work experience • A recognized undergraduate degree or equivalent • 70 % or a B average in prior post secondary and professional education Applicants without an undergraduate degree will be considered for admission provided they have achieved significant work experience at an intermediate management level and have demonstrated a strong academic aptitude. Contact your provincial affiliate office or international program office for more information if you are considering applying without a degree. Presentation of a GMAT score is not required for admission, but may strengthen an application. For more information on the admission requirements and application process, please contact your provincial
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.688 | 0.440 |
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".