Bibliographic record
Abstract
An empirically rich and student-friendly book in which global media expert Dal Yong Jin discusses the nexus of globalization, digital media, and popular culture and provides an essential introduction to the shifting media ecology of the early 21st century. Offering an in-depth look at globalization processes as they relate to the global media, this second edition maps out the increasing role of digital platforms as they continue to shift the contours of globalization. This book introduces core theoretical concepts—such as cultural imperialism, platform imperialism, and soft power—that can be critically applied to a broad range of contemporary media policies, practices, movements, and technologies in different geographic regions of the world, with a view to determining how they shape and are shaped by globalization. Fully updated throughout, this second edition explores new critical issues—such as the impact of COVID-19 and the growth of artificial intelligence (AI) in cultural production—emphasizing the ever-increasing role digital platforms play in the globalization process. It also introduces new theoretical frameworks for understanding globalization, such as transnational proximity. End-of-chapter discussion questions prompt further critical thinking and research. An essential book for students of digital media, global media, and globalization that want to understand the increasing impact of AI and digital platforms on global media and culture in the digital platform era.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".