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Record W4399596118 · doi:10.38080/crh.2024.05.147.163

Global Reparations for Historical Abuses of Children, Indigenous Peoples, and Minorities

2024· article· en· W4399596118 on OpenAlexaboutno aff
Yunjeong Joo

Bibliographic record

VenueCritical Review of History · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPolitical scienceCriminologyLawSociologyBiologyEcology

Abstract

fetched live from OpenAlex

Globally, past violence against children, minorities, and marginalized groups is being revisited and not merely concealed or forgotten. This is made possible by various factors, notably the overall increase in human rights awareness within society. One prominent aspect is the uncovering of past issues due to reports of violence in care facilities. Additionally, psychological research on trauma and adverse childhood experiences has highlighted that such harm is not merely a historical issue but one that affects individuals throughout their lives, bringing the problem into sharper focus.<br/>The spread of international norms that recognize justice from the perspective of victims and the emergence of restorative justice within criminal justice systems—emphasizing recovery over merely punishing perpetrators—have also played a role. Furthermore, this issue is not limited to newly independent nations undergoing state formation; developed Western countries are addressing internal colonialism and internal violence issues through transformative justice.<br/>For instance, the recognition of anti-social elements in Germany, child abuse in Nordic countries and Canada, issues concerning minorities in Norway, and the relationship with nature are being identified as tasks for historical recovery. These examples demonstrate that the task of addressing historical injustices is evolving from judicial justice to restorative and transformative justice processes, emphasizing the connection between the past and present. This shift underscores the recognition of these issues as structural inequalities.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.347
Teacher spread0.311 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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