Stressogenicity of Media Noise in the Conditions of Background Media Consumption
Bibliographic record
Abstract
The authors of the paper present for the first time the concept of media noise in the living space of modern children; they also introduce the concept of media noise as forced media consumption in a background (backdrop) format, and consumption in parallel with the main activity (foreground). The stressful effect of the operation of screens and players in the background on people in this space, and the impact of the inclusion of children in a continuous media stream are assessed. It is noted that pervasive media increase the potential of psycho-emotional impact through trance methods of exposure affecting the cognitive, affective and behavioural sphere of individuals, and this forces them to consume information, burden the information space of children, and qualitatively change the social situation of their development. The paper presents the findings concerning pilot studies of the media noise phenomenon and the state of media noise. The existence of a relationship between various parameters of forced background media consumption is shown; the main situations of background media consumption, as well as the reasons for the independent media noise initiation and its duration, are given. The aggravated negative impact of background media consumption in connection with the new life realities of mankind during the period of total immersion in the media space is noted; the last is caused by the need for social distance.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".