Bibliographic record
Abstract
My thesis is focused on problems of the e-learning in the world (especially with the situation in USA and Europe) and with the situation in Czech republic. The thesis is devided into several parts. The first part is the theoretical introduction. On this place I focus on positive and negative aspects of e-learning, the content and the advantages and disadvangetes of e-learning in constrast with the classic teaching. Technological aspect is very important too, because there is a progress in this area both the technological and organizational view. By studying the theoretical information a reader can get a solid base on the e-learning problems. In the second part of the text, I describe the present situation in this area. Especially the situation in Europe and in North America (Canada and USA). The third part of the text is devoted to the situation on the education systém in Czech republic. I am interested in the situation on primary, secondary and tertiary education. The importance is concentrated also on the legal regulations. In the last two paragraphs, I am interested in the analysis of the Faculty of informatics and statistics, the University of Economics, Prague faculty. I am trying to answer the question if it is possible to establish e-learning on this faculty.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".