Diaspora, memory, and ethnic media: media use by Somalis living in Canada
Bibliographic record
Abstract
In the United States, numerous studies have reported a significant increase in the use of ethnic media and their audiences.2 However, the area of research studying ethnic media, located at the intersection of media, minorities, and immigration, remains underdeveloped in Europe,3 which could explain the existing confusion about the concepts of “community media” and “ethnic media.” In France, instead of the concept of “ethnicity,” an elaborate list of euphemisms was constructed to serve as a semantic repertoire describing the same phenomenon,4 even though the phenomenon in question has been extensively defined.5 This confusion seems to be a product of disinterest on the part of the social sciences in France in the studies of minorities and interethnic relations, a phenomenon that involves multiple factors.6 Therefore, it appears pertinent to clarify these ambiguities. Ethnic media and community media are used in conjunction with three developments: international migration, increased privatization and commercialization of public spaces, and, lastly, the development of information and communication technologies. The development of community media is linked to the rise in privatization and commercialization of public spaces. Operating from the margins of mainstream media, community media offer a third voice in the mass-media system, after the private and public sectors.7 Ethnic and alternative media are also located in this third category.
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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 | 0.006 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".