Epistemic violence towards the mothers of colonial Métis children: evidence from Belgium’s ‘Africa archives’
Bibliographic record
Abstract
Between 1958 and 1961, as Ruanda-Urundi approached independence, the Belgian State deported 283 Métis children to Belgium where they were placed in orphanages or with foster families. Most of these illegitimate children of Black women and white colonial men would never see their birth mothers again, believing the ‘official’ narrative that their mothers had abandoned them. This article focuses on the mothers’ stories uncovered in visits to Belgium’s ‘Africa Archives’ in Brussels. Previously inaccessible for over 40 years, the files consulted debunk the notion that the mothers wilfully gave up their children. Instead, they expose the racial discrimination and prejudices to which the mothers were subjected by Belgian colonial officials determined to segregate the children from their African families and subsequently remove them to Belgium indefinitely. Using Miranda Fricker’s concepts of ‘testimonial injustice’ and ‘hermeneutical injustice’ as a theoretical framework, we examine the marginalization, coercion, and manipulation of the mothers in terms of epistemic violence facilitated by the colonial racial hierarchy. Additionally we suggest that, despite recent improvements pertaining to accessing Belgium’s colonial archives, the continued difficulties in accessing the files for the mothers and their families residing in Africa points to a perpetuation of colonial injustice.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".