The Effect of mass media on social security
Bibliographic record
Abstract
This study examines the impact of mass media on social security, for convenience and easy access to this type of research, we conducted it in the city of Islam Abad and then we can generalize it to the whole community of Iran. The population studied in this research is individuals and households of Islam Abad city that encompasses most of the student class. By using questionnaires, it has been tried that the operating variables such as the impact of mass media on social security have been assessed, and initial evaluation of the research has been given to researchers. The theoretical framework of this study is based on the opinions and ideas of the great scientists of the research. The research was based on field research and survey and data collected through the questionnaire and in the fourth quarter, inferential analyzes were applied. In this research, specializing software SPSS was used for analysis. Among sample population of 100 patients, 0.73 were female and 0.27 were male, 0.72 were single and 0.28 were married. The average monthly household income is 531,100 Tooman, and the average age is between14 to 24. In this chapter, we discuss the conclusions and with recommendations the research is terminated.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".