The Benin tusk and Zulu beadwork: Practicing decolonial work at Manchester Museum through shared authority
Bibliographic record
Abstract
Abstract The museum world is currently grappling with questions of how to decolonize anthropological collections and many of these debates are epistemologically oriented. In pursuit of colonial ordering, material culture was extracted from colonized societies, deprived of its contextual meaning, and scrutinized through the lens of colonial knowledge. This article considers how an empirical decolonial practice can be applied drawing on from the current work at the Manchester Museum (MM). Dialogue, open engagements, multivocal conversations, collaborations, and shared authority in knowledge production are some of the decolonial strategies that I share. To illustrate this praxis turn in museum decolonial work, I first look at how we have addressed cultural objects looted from Benin in 1897 that we hold and “contain” at MM in our living cultures collection, underscoring a commitment by MM to transparency and a provision of access to the living collection by different groups of people. The second example is drawn from a collaborative provenance research that I undertook with Nongoma community members in South Africa in rewriting biographies of Zulu beadwork that we house at MM. Overall, I argue that decolonization should embrace a relational practice of caring for objects through active relations of reciprocity and dialogue with communities. The downside of decolonial practices and how are they are inherently shaped by power imbalances and tensions between curators and communities is also critically discussed.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".