Attracting, preparing, and retaining under-represented populations in rural and remote Alberta-North communities
Bibliographic record
Abstract
<p>For several years, the government of the western Canadian province of Alberta has drafted policies and conducted research on the problem of populations under-represented in adult education. This Alberta-North and Athabasca University study, funded by the Alberta government’s Innovation Fund, uses the advice and educational experiences of northern former and present students, and of other community members, to identify ways of better attracting, preparing, and retaining under-represented populations in northern Alberta communities through provision and training in the use of distance delivery methods.</p><p>The research reported here commences with a review of the literature to investigate the following: 1) the contribution distance education makes globally to learning access in remote areas (and resulting economic growth for under-served populations); 2) how support is provided to retain isolated students; and 3) the help needed to assist remote students to complete distance programs. Community consultations with social service and education agencies in three communities were conducted in order to obtain their perspectives about what helps to attract and support students to educational programs and the barriers students typically encounter, which might be mitigated by distance methods. Finally, a survey was designed and distributed in 87 Alberta-North communities in northern Alberta and across Canada’s Northwest Territories to add perspective to the consultation results.</p>
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".