The Alphabet Effect Re-Visited, McLuhan Reversals and Complexity Theory
Bibliographic record
Abstract
The alphabet effect that showed that codified law, alphabetic writing, monotheism, abstract science and deductive logic are interlinked, first proposed by McLuhan and Logan (1977), is revisited. Marshall and Eric McLuhan’s (1988) insight that alphabetic writing led to the separation of figure and ground and their interplay, as well as the emergence of visual space, are reviewed and shown to be two additional effects of the alphabet. We then identify more additional new components of the alphabet effect by demonstrating that alphabetic writing also gave rise to (1) Duality, and (2) reductionism or the linear sequential relationship of causes followed by effects. We then review McLuhan’s (1962) claim that electrically configured information reversed the dominance of visual space over acoustic space and led to the reversals of (1) cause and effect, and (2) figure and ground. We then demonstrate that General System Theory first formulated by Ludwig von Bertalanffy (1968), which also includes chaos theory, complexity theory and emergence (aka emergent dynamics) and Jakob von Uexküll’s (1926) notion of umwelt also entail the reversal of many aspects of the alphabet effect such as the reversals of (1) cause and effect, and (2) figure and ground.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".