Bibliographic record
Abstract
Abstract Ethnic media have been studied consistently across various regions since the early 1900s. This chapter reflects on ethnic media research in the digital age, specifically focusing on research published in the past two decades. The purpose is to understand how ethnic media have been conceptualized and researched, and to suggest future research directions. This reflection identifies the persistent conceptualization of ethnic media as “media for the Other,” with increasing attention to the broader role of ethnic media as “media beyond the Other.” This reflection also identifies three approaches to ethnic media research: assimilationist/pluralist, journalistic/media-centric, and interdisciplinary approaches. Among these approaches, the journalistic/media-centric and interdisciplinary approaches were notable. As attempts to move beyond the assimilationist/pluralist binary, the journalistic/media-centric approach tends to explore the production, consumption, and content of ethnic media within or in relation to the broader societal context of social, economic, political, policy/regulatory, and technological factors, while the interdisciplinary approach tends to emphasize hybrid diasporic identities of migrants and their sense of belonging and media practices in a transnational context. Future research requires more attention to ethnic media in the Global South, the diasporic nature of ethnic media, and the intercultural role of ethnic media.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| 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".