Bibliographic record
Abstract
It is undeniable that distance learning has grown rapidly over the past five years. With over 12 billion dollars spent on online learning in 1998 (Burgess & Russell, 2003) and a growth rate of 30%-40% per year since then (Harper, Chen, & Yen, 2004; Hurst, 2001; Newman, 2003), it is safe to say that distance education is firmly established in many businesses and universities. One well-established advantage of distance learning is that a student controls the time, pace, and pathway of learning (Burgess & Russell, 2003; Pierrakeas, 2003). This control over learning is very appealing to a user, particularly when customized or just-in-time support is readily available (Harper, Chen, & Yen, 2004). Providing effective, timely support, though, puts considerable strain on instructors and tutors, if they are available (Harper et al., 2004; Levine, 2003; Wallace & Wallace, 2001). It is challenging to provide just-in-time help because delay is inevitable. The use of e-mail or online discussion necessitates a time lag between question and response. Instant messaging systems (IMS) are another option, however, it is cost prohibitive to have instructors and tutors available 24 hours a day, 7 days a week. Furthermore, IMS might be limited in the type of question that could be answered – complex formulas and equations, for example, are difficult to explain using this medium.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.127 | 0.042 |
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".