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Record W2778541988 · doi:10.1080/13527258.2017.1413681

Speaking for the dead: the memorial politics of genocide in Namibia and Germany

2017· article· en· W2778541988 on OpenAlexafffund
Ronald Niezen

Bibliographic record

VenueInternational Journal of Heritage Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicGerman Colonialism and Identity Studies
Canadian institutionsMcGill University
FundersCanada Research Chairs
KeywordsGenocidePoliticsPolitical scienceHistoryAncient historyArchaeologyEconomic historyEthnologyLaw

Abstract

fetched live from OpenAlex

This paper discusses the politics of the material commemoration of mass crime, with a focus on the Ovaherero and Nama descendants of the victims of a 1904–1908 mass ethnic killing in German Southwest Africa. My approach to monuments emphasises their place as artefacts that mark changes of regime after war or revolution, and as focal points of resistance to state regimes of commemoration. Tracing the material forms of memorialisation in Germany reveals the significance of both a ‘remembrance culture’ of the Holocaust and, at the same time, resistance to recognition of the Ovaherero/Nama genocide. In Namibia, the success of the Ovaherero/Nama activist campaign in Germany prompted the government to shift positions and take up the cause of genocide remembrance, asking Germany to officially recognise that its actions constituted genocide, to issue a formal apology and to pay reparations. By framing the mass violence of imperial Germany in terms of its enduring legacy in heritage, Ovaherero and Nama activists and their supporters were able to cross into different geographies of commemoration and bring distant wrongs, without living witnesses, into the present.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.012
Scholarly communication0.0060.003
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.067
GPT teacher head0.340
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2017
Admission routes2
Has abstractyes

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