THE INTERACTION OF HUMANS AND THE INFORMATION SPHERE IN THE DIGITAL AGE
Bibliographic record
Abstract
The article presents the results of the research devoted to the study of the peculiarities of human interaction with the information sphere in the digital age. In particular, the impact of information technologies on human thinking and behavior, social communications and relationships is studied. Special attention is paid to digital platforms and social networks that change human reality, transforming the material context into virtuality. The positive and negative sides of using social networks are also considered, as well as the possible consequences of their use in the long term. In the process of research, the role of information technologies in the modern world and their influence on human behavior and thinking is revealed, the specifics of human interaction with information in the era of information technologies are analyzed, the social and psychological consequences of the use of information technologies and social networks for people are identified, recommendations are developed for the correct use of information technologies and social networks in order to ensure a balanced and healthy human life. It is argued that modern IT has enabled unprecedented access to information and brought people together in new ways, but at the same time created significant challenges such as information overload and the spread of misinformation. It is proven that the development of artificial intelligence and automation of processes is one of the latest trends in the evolution of human interaction with the information sphere, which may eventually lead to radical changes in production processes and the situation on the labor market. The issue of responsible and ethical handling of information technologies is raised due to the significance of their impact on cultural processes on a global scale. The author's argumentation is strengthened by the ideas and opinions of leading modern thinkers on this topic.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".