CANADA - INCLUSIVE DISTANCE EDUCATION: Experiences of Four Canadian Women
Bibliographic record
Abstract
Women's participation in higher education in Canada has changed over the past two decades and no longer is the gender gap in university attainment in favour of men. Today young women are graduating from university in higher numbers than are men. Even those women who, for one reason or other, are unable to attend traditional universities are also choosing to participate in higher education. Women not only make up the majority of university graduates they also make up the majority of distance education users. Online and distance education enables many adult women, particularly those who assume multiple roles as mothers, professionals, caregivers and academics, to continue their formal learning. In this paper, framed within the context of feminism, we share with you the stories of four ambitious and successful professional Canadian women studying at a leading Canadian online university. A review of the literature is also conducted, largely from a US and Canadian view. Canada proves to be a country that values the complexity of the multiple roles that women assume daily and inclusively supports women’s choice to pursue post-secondary education or graduate work. We examine the organization of tertiary education in Canada, consider the vast geography of one of the most richly diverse countries in the world, and factors that govern both a woman’s decision to seek higher education and; assist in learner satisfaction and retention rates for women in tertiary education. Four experiences of graduate women confirm that Canada’s inclusive approach creates satisfied life-long learners.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.054 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".