Diffusion and impact of Marshall McLuhan's published work in the Web of Science
Bibliographic record
Abstract
This study gauges the scientific impact of Marshall McLuhan’s works on academic research. To this end, an analysis was undertaken to study published research that cited his works in the Web of Science (WoS) from 1957 to 2017. A total of 6,591 documents were found that record 8,989 citations from a range of McLuhan’s, mainly scientific documents (journal articles and monographs). The temporary distribution of the documents, the citations received by the different types of documents, the subject matter of the papers and the other co-authors cited along with McLuhan were analyzed. Among the main results, it was found that compared to the Canadian researcher’s journal articles, his monographs received the most citations received, notably his work entitled “Understanding Media: The Extensions of Man”. In addition, it was discovered that the interdisciplinary nature of McLuhan’s thought has had repercussions in different fields, such as communication, education, sociology and computer science. Since the second half of the 20th century, McLuhan is a benchmark, together with outstanding sociologists, philosophers and communication theorists such as Theodor Adorno, Pierre Bourdieu and Zygmunt Bauman, among others.
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 | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.028 | 0.038 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".