Eternal Present? From McLuhan’s Global Village to Artificial Intelligence
Bibliographic record
Abstract
Herbert Marshall McLuhan (1911-1980) was a Canadian philosopher, philologist, literary scholar and media expert. Andy Warhol famously said that in the future everyone will be famous for fifteen minutes. As for McLuhan’s fame, it stretched over decades. His ideas that technology can influence and shape society have been widely discussed up to this day. McLuhan’s work is regarded as an important conceptual contribution to media theory, and his term ‘global village’ is widely used by students and scholars, practitioners and theorists in the field of communication. The authors of this article attempt to analyze the contribution of McLuhan, as a communication expert and influential technodeterminist, to understanding the media of the 21st century. It is concluded that, despite the abundant criticism regarding the naivety and “unscientific” nature of his approach, the lack of empirical evidence for the theory he put forward, his passion for technology, as well as his belief in the decisive role of the media in the development of culture and society, McLuhan’s ideas still inspire researchers. The concept of ‘global village’ in the modern information world has not only retained its relevance - it describes the laws of the functioning of the digital society in the best possible way. The Internet and social networks have confirmed Marshall’s postulate that communication technologies enable people to become increasingly involved in one another’s lives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".