Bibliographic record
Abstract
This study examines earning a Canadian Master's degree as a lifelong learning pursuit. Data from Statistics Canada's, national, 1997 Survey of 1995 Graduates are used to profile 1995 Master's graduates by graduation age and gender using the age groups, under 25, 25 - 29, 30 - 34, 35 - 39, 40 - 44, 45 - 49 and, 50 and over. Cross-tabulations and chi-square tests are utilized to identify significant relationships and trends. Research questions addressed are: how did the study profiles of Master's graduates by age group and their gender subgroups, differ by region, discipline, reasons for enrolling, study mode (part-time, full-time or combination) and/or educational funding sources? Additionally: how did the post-graduation profiles of Master's graduates by age group and their gender sub-groups, differ by accumulated debt, job search experiences, job characteristics, job- education match, income and/or plans to pursue a Ph.D.? -- It was found that as age increased, so did the percentage of graduates who were female, studied part-time, had no difficulty in the job search, supervised the work of other employees, and earned $50,000 or more per year. The percentage of graduates who said the chance to earn a good income was a very important reason for enrolling and who utilized scholarships decreased as graduation age increased. Graduates aged 40 and over were concentrated in the Humanities, Social Science, Education and Commerce related disciplines. Graduates from Ontario and Quebec (both genders) and males from the Western Provinces and Territories were typically younger than those from other Canadian regions. Less than 35% of 1995 Master's graduates in any age group indicated that they planned to pursue a Ph.D.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".