Bibliographic record
Abstract
Media Travels: Toward An Atlas of Global Media fills a significant gap in global media scholarship by offering short, readable articles covering different types of media from around the world. Through careful and informed analysis, these eleven accessibly written chapters illustrate the particularities of different media practices and situate them within social, historical, and geographical contexts. Examples range from South African video games to Korean TV series popular in Latin America to Indigenous film and media from the US and Canada. Media studies courses, particularly introductory courses, are often narrowly focused on US and Western European canons. Instructors for introductory media studies courses wishing to expand the offerings in their curricula will find in these essays new ways of approaching foundational concepts and issues in the field, including globalization, social difference, and diverse media cultures. Scholars wishing to expand their research into specific media forms or representational issues can also turn to these case studies for approaches from beyond the US. By including a variety of media and several geographical areas, the collection introduces readers to the formal, technological, and cultural diversity of global media studies. Edited by Juan Llamas-Rodriguez with contributions from Anthony Adah and Añulika Agina, Maria Corrigan, Benjamin Han, Anna Shah Hoque, Meryem Kamil, Angelica Marie Lawson, Lilia Adriana Perez Limon, Sonia Robles, Kuhu Tanvir, David Tenorio, and Rachel van der Merwe.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.934 | 0.923 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".