Bibliographic record
Abstract
The present study was conducted in Parbhani district of Marathwada region of Maharashtra state. Half of urban and half of rural respondents were selected. Villages were selected in the radius of 10 km. from Taluka head quarter, where the maximum numbers of television sets were installed. Data were collected from 150 female respondents from four villages including Parbhani. An interview schedule was specially structured for data collection. The data were analyzed by using frequencies and percentages. From the study, it was found that majority of the respondents placed the highest credibility on TV as a source of technical and general information. Agricultural and home science programmes were viewed regularly by the majority of the respondents. A great majority of the respondents were aware of the timings of both of the telecasts but they did not use the information actually given through the telecasts. The maximum numbers of the respondents were satisfied with the time apportioned for the telecasts. Maximum televiewers expressed that the programmes were easy to understand and demonstration with talk was the better mode of presentation. Majority of the respondents had discussions with others about the programmes, whereas few of them were interested in taking the important notes about the programmes.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".