The development of distance education in the Russian Federation and the former Soviet Union
Bibliographic record
Abstract
<!-- @font-face { font-family: "Cambria Math"; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0cm 0cm 0.0001pt; line-height: 200%; font-size: 12pt; font-family: "Times New Roman"; }p.Abstract, li.Abstract, div.Abstract { margin: 18pt 1cm 15pt 36pt; line-height: 150%; font-size: 11pt; font-family: "Times New Roman"; }p.AbstractCxSpFirst, li.AbstractCxSpFirst, div.AbstractCxSpFirst { margin: 18pt 1cm 0.0001pt 36pt; line-height: 150%; font-size: 11pt; font-family: "Times New Roman"; }p.AbstractCxSpMiddle, li.AbstractCxSpMiddle, div.AbstractCxSpMiddle { margin: 0cm 1cm 0.0001pt 36pt; line-height: 150%; font-size: 11pt; font-family: "Times New Roman"; }p.AbstractCxSpLast, li.AbstractCxSpLast, div.AbstractCxSpLast { margin: 0cm 1cm 15pt 36pt; line-height: 150%; font-size: 11pt; font-family: "Times New Roman"; }.MsoChpDefault { font-size: 10pt; }div.WordSection1 { page: WordSection1; } --><p class="Abstract">Distance education in the present Russian Federation and former Soviet Union has a long tradition that prevails to this day. The majority of students in Russia are enrolled in distance learning programs. The numbers indicate the existence of a well-established system for distance education, of which little is known in Western literature. A review of distance education research in the Anglo-American sphere showed that within the past 10 years not a single article dealing with the Russian system was published. Consequently, within international DE research Russia remains uncharted territory. The following explorative study introduces the educational and tertiary educational system and presents current statistical data while emphasizing the historical perspective to further describe how the distance education system is embedded therein. In order to discuss current practice in this field, one of the biggest higher distance education institutions in Moscow with approximately 110,000 students is used as an example.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".