Hope in Africa? Social representations of world history and the future in six African countries
Bibliographic record
Abstract
Data on social representations of world history have been collected everywhere in the world except sub-Saharan Africa. Two studies using open-ended data involving university students from six African countries fill this gap. In Study 1, nominations from Cape Verde and Mozambique for the most important events in world history in the past 1000 years were dominated by war and politics, recency effects, and Western-centrism tempered by African sociocentrism on colonization and independence. The first three findings replicated previous research conducted in other parts of the world, but the last pattern contrasted sharply with European data. Study 2 employed a novel method asking participants how they would begin the narration of world history, and then to describe a major transition to the present. Participants most frequently wrote about the evolution of humanity out of Africa, followed by war and then colonization as a beginning, and then replicated previous findings with war, colonization, and technology as major transitions to the present. Finally, when asked about how they foresaw the future, many participants expressed hope for peace and cooperation, especially those facing more risk of collective violence (Burundi and Congo). A colonial/liberation narrative was more predominant in the data from former Portuguese colonies (Angola, Cape Verde, and Guinea-Bissau) than from former Belgian colonies (Burundi and Congo).
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".