Social Issues of People with Special Needs in Modern Mass Media
Bibliographic record
Abstract
This research paper discusses the problem of social issues, especially the issues of people with intellectual disabilities and how modern mass media supports. Mainly one case study was represented from aspects working with people with ID. This study aimed to examine how the press represents people with intellectual disabilities and how it helps people with this problem. The study was conducted using the method of qualitative content analysis. The material for the report consisted of research. The survey covered the year 2016. The purpose of this qualitative study was to produce an overview of topics and practical recommendations that have been presented for teaching for students with intellectual disabilities. To represent the richness of this research area, the topic was purposely left broad, and the outline was made by focusing on the practical implications of research articles. These recommendations were identified, classified, synthesised, and evaluated. The implications for practice and research are presented based on the findings of this study This research is of significant value, as a contribution to the journalism science policy not only in Kazakhstan but abroad as well. It demonstrates that social issues of the problems of people with ID in modern mass media are widely discussed and are of great importance from the sight of an audience.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".