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Record W1974766183 · doi:10.1111/1521-9488.t01-1-00261

Justice and Moral Regeneration: Lessons from the Treaty of Versailles

2002· article· en· W1974766183 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Studies Review · 2002
Typearticle
Languageen
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsMcGill University
Fundersnot available
KeywordsTreatyEconomic JusticeRetributive justiceLawSociologyPolitical scienceLaw and economics

Abstract

fetched live from OpenAlex

The Treaty of Versailles, which concluded World War I, aimed to establish a “peace of justice”; sadly, it only seemed to pave the way to a second, more devastating world war. What lessons about justice and reconciliation can we learn from the treaty and its apparent failure? Some scholars argue that the fault of the treaty lay in its preoccupation with retributive justice, undermining prospects for reconciliation. Rather than positing justice and reconciliation as inherently conflictual moral values or goals, both need to be conceived as part of the project of moral regeneration. Such a multidimensional project requires a certain kind of justice and reconciliation, founded on mutual respect for the humanity and equality of others. An assessment of the relationship among truth, justice, and reconciliation in the framework of moral regeneration indicates that the most grievous moral fault of the Treaty of Versailles lay in its process, which facilitated neither a truthful accounting of the war's causes and consequences, nor the affirmation of moral truths by victors or vanquished. The lack of an authoritative and public moral accounting of the Great War undermined both justice and reconciliation in international society.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.660
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 head0.419
GPT teacher head0.381
Teacher spread0.037 · 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