MEDIA IN THE ERA OF GLOBALIZATION IN LATIN AMERICA
Bibliographic record
Abstract
Terms like Internet, cyberspace, virtual reality, in short, globalization, have been frequently words for all of us, in recent years. This refers to the phenomenon that has shaken the people of this world. Are rapidly changing due to technological advances and complex levels reaching relations between countries, corporations, partnerships and people.The attempt to understand the phenomenon of globalization is compounded when we try to understand the term, coined by Marshall McLuhan.McLuhan was born in Edmonton, Alberta, Canada and studied at the Universities of Manitoba and Cambridge, the latter of which he received his degree of Doctor of Philosophy specializing in English Literature. He taught at the universities of Wisconsin and St. Louis and University of the Assumption and Saint Michael's College, University of Toronto, where he was director of the Center for Culture and Technology.Marshall McLuhan and B. R. Powers, wrote the play, The Village Global1. The universe has become a village is the future predicted for them in the 60's. Today reality has overtaken the theory. However, this phenomenon is presented in this work, so whimsical style reminiscent of Jules Verne, but does not clarify the content of the term. While we believe that in the past there were attempts; globalize these attempts were very different from what we understand by globalization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".