The Rise of McLuhanism, The Loss of Innis-sense: Rethinking the Origins of the Toronto School of Communication
Bibliographic record
Abstract
This article compares why McLuhan’s work in communications has been the source of much acclaim whereas that of Innis has attracted attention only recently. It argues that the disparate responses to the contributions of these two theorists are rooted not only in the extent to which their writings were available, but also in their differing communication practices. The latter account for why Innis’ studies of media were initially ignored and why McLuhan was able to develop a considerable following, in part by drawing on Innis as a precursor; this resulted in a distorted view of Innis’ ideas that has persisted to this day. As a corrective, the article challenges McLuhan’s claims that Innis viewed media as a form of staple and that he sought to understand how various knowledge specialities could be unified. Finally, it makes the case that the Innis/McLuhan tandem should be decoupled, to make better sense of a “de-McLuhanised” Innis on the one hand, and the McLuhanist-centred Toronto School on the other.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.092 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| 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".