Bibliographic record
Abstract
The paper is dedicated to the Toronto School of Communication and to Harold Innis, who is recognized as the founding father of this school even by its most famous representative, Marshall McLuhan. The analysis focuses on the central concept of Innis’ work, that of bias of communication. Innis’ inquiry is mainly historical, meaning that the author seeks to identify the influence of media in creating a time or a space bias. Despite this historical character, the core of his investigation is the bias of contemporary civilization. According to the Canadian author, this bias is a consequence of the monopoly of knowledge created by the prevalence of written press, by its obsession with space at the expense of time. Contemporary civilization is excessively concerned with the present time and obscures both past and future. A certain type of rigidity emerges, accompanied by reluctance to explore and exploit other dimensions then the space-related ones. The paper makes some clarifications regarding the “technological determinism” usually associated with the Toronto School. The analysis shows that this is an oversimplification, since the core of Innis’ work is that only the balance between space and time can provide for the long-term survival of civilizations.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".