1Running head: DISTANCE EDUCATION AND WOMEN Final Project Letter of Intent Campus of Alberta Applied Psychology: Counselling Initiative Issues and Perspectives in Women and Distance Education: A Guidebook
Bibliographic record
Abstract
Distance learning has been in existence for at least 100 years, and due to changes in technology, has advanced rapidly in the last forty years (Galusha, 2004). Distance Education (DE) is defined by Rezabek (1999) as: The transportation of information and the involvement of a learner in the acquisition of knowledge and understanding of an area of study through planned, usually structured, and organized (but also incidental) communication, that also uses supplemental resources and media-assisted two way communication, where the learner and instructor are separated by distance and/or time. (p. 12) DE provides the opportunity for study with freedom in relation to time and place of study for those unable to attend traditional classroom based instruction. It allows for a high degree of flexibility for individuals pursuing education for professional purposes or as part of their leisure pursuits. Consistent with this perspective, Chung (1990) advocated that: Distance education provides opportunities for adults to change careers later in life; to enhance their skills and qualifications while retaining their jobs; to bring up a young family while continuing with their education; to keep up with ever-changing technologies; and to
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.329 | 0.049 |
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".