The Use of Tetrads in the Analysis of Arts-Based Media
Bibliographic record
Abstract
In this article, we chose the musical form of a sonata to examine tetrads, a simple four-fold structure that Marshall McLuhan coined and employed to describe various technologies. Tetrads, as cognitive models, are used to refine, focus, or discover entities in cultures and technologies, which are hidden from view in the psyche. Tetradic logic frames human artifacts and the means of doing things. The ideas that McLuhan eloquently brought to consciousness, long before technologies became the sophisticated communication tools they have become today, may be reinterpreted in a far more timely fashion. The poignancy of his views invite our immediate attention in light of the limitless extensions humans are being afforded with new technologies. McLuhan has always remained a significant and powerful voice among artists—his ideas, in effect, resonate with our artistic sensibilities.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".