Marshall McLuhan’s General Theory of Media (GToM), His Laws of Media; Comparing Three Kinds of Law
Bibliographic record
Abstract
We suggest that despite McLuhan’s claim not to have a theory of communication that in fact the body of his work does indeed constitute a theory of media and their effects which I have called his General Theory of Media (GToM) that also includes his Laws of Media (LoM). Both McLuhan’s GToM and his LoM are described. A comparison is made of three notions of law: i. McLuhan’s notion of law as used in his Laws of Media; ii. the notions of the Law in the legal sense and iii. the notion of law as formulated in scientific laws. McLuhan’s understanding of media is used to analyze some of the negative effects of social media suggesting that laws need to be formulated to prevent the misuse of social media that are antithetical to democracy and the invasion of the privacy of the individual users of these apps. McLuhan’s Laws of Media are then used to provide insights into the nature of scientific laws, the Law in the legal sense and his own Laws of Media.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".