aenjournal Representing Ethnic Communities in the Media
Bibliographic record
Abstract
Does ethnicity matter in the media? If so, when and what is the role of ethnic and mainstream media in promoting the good news as well as the bad news about migrants and refugees? The results of an AEN quick-survey suggest that mainstream media is a key cross-over point for inter-cultural exchange and a primary vehicle for promoting inter-cultural awareness and understanding. Ethnic media publications provide an alternative to an increasingly homogenised mainstream media. They are essential to the health of a civic society and make an essential contribution to promoting and sustaining social movements. Communicating horizontally, rather than from the top down, ethnic media help to build communities, reduce social isolation and keep culture and language alive. They are able to bring about social change from within communities (Lalley & Hawkins, 2005). The news media play a major role in society around establishing and disseminating cultural references and are pivotal in representing and giving voice to community members. However, they can unintentionally strengthen racist discourses rather than fighting them (ERCOMER, 2002). According to a California New Media study, nearly a quarter of all U.S. residents regularly get information from the ethnic media (Briggs, 2005). A national, multilingual poll of almost 1,900 Latino, African-American, Asian-American, Arab-American and American Indian respondents showed that 13 % of
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.019 |
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".