The Scramble for African Media: The British Government, Reuters, and Thomson in the 1960s
Bibliographic record
Abstract
With the wave of independence in Africa in the late 1950s and early 1960s, the British government sought not only to maintain influence in former colonies but also to expand it in the rest of the continent. One lever of influence was news media. The British government encouraged and subsidized London-based media to expand in Africa to block the Soviets, forestall competitors, and preserve British influence in a classic example of media imperialism. The Reuters news agency used a secret subsidy to greatly expand its Africa coverage and sign up new national governments as clients. Anglo-Canadian newspaper millionaire Roy Thomson, who sought a peerage, cooperated closely with the government as he invested heavily in African newspapers and television systems and journalism training.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".