Bibliographic record
Abstract
Following months of political instability in Nigeria and the massacres of Igbo people in the north of the country during the summer and fall of 1966, the situation deteriorated rapidly. On 30 May 1967, the Eastern Region (Igboland) unilaterally declared its independence, triggering a civil war that, coupled with a tight economic blockade of the secessionist region and the logistic support of foreign powers, turned into a three-year conflict that claimed over three million lives. Nigerian and Biafran troops were engaged in battle from 6 July 1967 until 12 January 1970, when the war ended with Biafra’s surrender. All through the conflict, but especially from 1968—9, the world media converged on Nigeria, trying their best to cover the conflict for their audiences, with mixed fortunes. This study, based on radio bulletins gathered daily in France from 1968 to 1970 from the BBC World Service, Voice of America, France-Inter, Radio-France International, Europe n.1, Radio-Brussels, Radio-Lausanne and Radio-Canada, reveals the way the media groped their way through a flurry of contradicting nuggets of information in a desperate hunt for the truth on a far-away nightmare, with unreliable sources contributing to the confusion and a proliferation of unreliable pieces of news. In spite of these limitations, the media succeeded in covering the progress on the ground, the unfolding of the humanitarian situation and mounting casualties, the arms race and the political scene, and eventually achieved their aim — that of attracting the world’s attention to Nigeria.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".