Reader Gujarat University Title(s) and Name(s) of the person(s) making the presentation at the Forum
Bibliographic record
Abstract
India has developed an effective delivery system across the country in the form of national network connected through Indian National Satellite Service, which is known as Doordarshan. A large number of programmes are being produced by Government as well as private agencies with the rising number of channels and concept of cable television and pay- TV. In India right from the inception of television, education has remained the main objective. Doordarshan spends more than 35 hours a week for school and college education (Chandrasekhar '97). The private sector also has special channels and special programming (Joshi 1998). India does not lag behind in comparison with other countries. In Japan NHK the sole public service broadcasting has a strong educational service character (NHK 1992). In USA Public Broadcasting System- PBS broadcasts 140 hours of programme a week. Most of these are educational programmes, scientific documentaries, and classical music concerts. (UNESCO 1997) In the UK the Open University uses television for 35 hours a week. In China it is used for 32 hours and Canada it is used for 12 hours a week (Haider 1998). The University Grants Commission on August 15 1984 started the regular INSAT television programmes for higher education. The telecast aims to enrich update and upgrade the quality of education for
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.681 | 0.617 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".