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Memory/Remedy

2023· book-chapter· en· W4386826671 on OpenAlexaff
Chigbo Arthur Anyaduba, Benjamin Maiangwa

Bibliographic record

VenueOxford University Press eBooks · 2023
Typebook-chapter
Languageen
FieldArts and Humanities
TopicHistorical and Contemporary Political Dynamics
Canadian institutionsLakehead UniversityUniversity of Winnipeg
Fundersnot available
KeywordsScholarshipInjusticeArgument (complex analysis)Transitional justicePoliticsPolitical scienceCriminologySociologyLawMedicine

Abstract

fetched live from OpenAlex

Abstract The chapter provides a provisional critique of transitional justice practices and scholarship advancing memory as a remedy to historical injustices. The chapter contends that certain uses of traumatic memory as a remedy to past injustices often attend the serious questions of injustice in terms suggestive of trauma healing. The emphasis on trauma and on the vocabulary of healing and reconciliation that have gained increasing currency in recent years often distracts attention from the apparent political preconditions of violence which in most cases have remained unresolved in the aftermath of mass violence. The chapter cites the example of the Biafra-Nigeria War (1967–1970) to elaborate our argument that this discourse on traumatic memory is inadequate for addressing troubled pasts. In making this point, the authors agree with the scholarship emphasizing revolutionary political reforms that attend to the political preconditions of violence.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.185
Teacher spread0.125 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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