Bibliographic record
Abstract
Many applications and tools have been developed to support the design and delivery of distance learning courses. Unfortunately, many of these applications have only cursory provisions for security and privacy, such as authentication based only on user id and password. Given the increased attacks on networked applications and the increased awareness of personal privacy rights, this situation is unacceptable. Indeed, electronic services of all kinds, including distance learning, will never be fully successful until the users of these services are confident that their information is protected from unauthorized access and their privacy assured. In the literature, there are few papers dealing specifically with security and privacy for distance education. El-Khatib, Korba, Xu and Yee (2003) discuss security and privacy for e-learning in terms of legislative requirements, standards and privacy-enhancing technologies. Korba, Yee, Xu, Song, Patrick and El-Khatib (2004) investigate how security and privacy can promote user trust in agent-supported distributed learning. Yee and Korba (2003, 2004) discuss the use and negotiation of privacy policies for distance education. Lin, Korba, Yee and Shih (2004) describe the application of security and privacy technologies to distance learning tools. Yee, Korba, Lin and Shih (2005) present an approach for using context-aware agents to implement security and privacy in distance learning. Holt and Fraser (2003) discuss the psychological and pedagogical motivation for security and privacy.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".