Bibliographic record
Abstract
In the past 10 years, a considerable amount of money and effort has been directed toward distance education, with growth estimated as high as 30%-40% annually (Harper, Chen, & Yen, 2004; Hurst, 2001; Newman, 2003). The popularity of distance learning appears to be founded on personal control over instruction (Burgess & Russell, 2003, Pierrakeas, 2003), the variety of multimedia formats available to students (Hayes & Jamrozik, 2001), and customized support (Harper et al., 2004). However, the success of distance education is anything but a foregone conclusion. Multiple obstacles have impeded acceptance including reluctance to use technology (Harper et al., 2004), time required to develop course resources (Harper et al., 2004; Hayes & Jamrozik, 2001) and to support students (Levine & Sun, 2002), lack of technology skills (Berge & Smith, 2000), and cost (Burgess & Russell, 2003; Levine & Sun, 2002). In addition the promise of interactivity and constructive learning in distance learning has not been realized. Most distance learning offerings resemble traditional classroom courses (Coates & Humpeys, 2003; Levine & Sun, 2002, Navaro, 2000). When interaction does take place, it is usually in the form of online discussion, however, a number of studies have reported that true social interaction leading to cognitive development is rare (e.g., Berge and Muilenburg, 2000; Bisenbach-Lucas, 2003; Garrison, Anderson, and Archer, 2001; Hara, Bonk and Angeli, 1998; Meyer, 2003; Wickstrom, 2003).
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".