Bibliographic record
Abstract
As computer networks evolve, the variety and quantity of machines available and the quantity of links used is increasing. In fact, each type of network has its own specific logical setting, switching mode, data format and level of quality of service (QoS). This explains, in part, the existence of heterogeneous environments for public and private networks of boundless dimensions giving rise to many problems of incompatibility (Stav & Tsalapatas, 2004). E-learning environments must address such problems. Many problems remain to be solved before e-learning is widely adopted and deployed by organizations. Initial training in the public education sector, professional training, and personal training at home are merging. Any useful, computer-based training solution must provide flexible learning systems, outside and inside the education system, before, after and during office hours. In all sectors, simplistic or inefficient use of the Web has yet to be overcome in order to offer an interesting alternative to the eyes of the client organizations. Currently, most e-learning material is focused on transmitting information. While this is undoubtedly useful, a shift to knowledge-intensive learning/training environments has yet to be made in order to address knowledge and skill shortages in a rapidly changing economy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".