“Othering” and “Others” in Religious Radio Broadcasts in Tanzania: Cases from Radio Maria Tanzania and Radio Imaan
Bibliographic record
Abstract
This article presents part of the findings of ongoing research on two religious radio stations and their audiences in Tanzania: Radio Maria Tanzania, owned by the Association of Radio Maria Tanzania; and Radio Imaan, owned by the Islamic Foundation based in Tanzania. Investments in religious radio stations are a product of the liberalization of the broadcasting sector which took effect in the 1990s in Tanzania. As a result of the liberalization, as of July 2011 the Tanzania Communications Regulatory Authority had registered seventy-five radio stations, twenty-six of which were owned by religious organizations. The proliferation of religious radio stations in Tanzania has changed the media landscape as well as Julius Nyerere’s Ujamaa version of African socialism. Nyerere’s socialism prohibited private media and the inclusion of aspects of ethnicity and religion in the public domain because of their divisive tendencies. Conceptualized by Spivak’s theory of othering, this article examines the othering strategies and “others” in increasingly religious radio stations. The collection of data for this study was done through interviews, qualitative content analysis, and discourse analysis. The findings show that the proliferation of religious radio stations in Tanzania perpetuates the othering tendency of religions to the extent of threatening the peace and unity Tanzania has experienced since independence in 1961.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".