Aproximación cienciométrica a la investigación en comunicación : el caso de Marshall McLuhan
Bibliographic record
Abstract
La aplicación de la técnica Cameo (Characteriza\ntions automatically made and edited online) a la trayectoria\ninvestigadora de Marshall McLuhan, una de las metodologías\nde análisis y representación de la información científica más\navanzada, permite construir un retrato científico del teórico\nde la comunicación canadiense y responder a cuestiones como\ncuáles fueron sus intereses profesionales y académicos, qué\nautores influyeron en sus teorías y cómo ha evolucionado su\npensamiento en los estudios de Comunicación. Analizando la\ninformación relacional de citas contenida en los artículos so\nbre McLuhan del ISI Web of Science es posible crear un mapa\ncon su trayectoria -reconociendo a los autores que más han in\nfluido en su carrera profesional- y una segunda representación\ncon las ‘huellas' que ha dejado su pensamiento en el panorama\nactual. Se aplican técnicas bibliométricas avanzadas para in\nvestigar en Comunicación y en Ciencias Sociales. | The application of the Cameo (Characterizations\nAutomatically Made and Edited Online) bibliometric techni\nque -one of the most advanced methodologies for the analy\nsis and representation of information- to the research history\nof Marshall McLuhan allows us to create a scientific portrait\nof this Canadian communications expert. The Cameo portrait\nidentifies McLuhan's professional and academic interests, the\nauthors that most heavily influenced his wave-breaking theories\nand the evolution of his line of thought in current trends of Com\nmunication. By analysing the relational information of citations\ncontained in articles about McLuhan in the ISI Web of Science,\nwe were able to create a map of his intellectual sojourns, inclu\nding signposts of the most influential authors in his professional\ncareer, along with a second representation that shows the 'foo\ntprints' his writings have left in the world.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".