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Record W2955639230

Narratology, Rhetoric, and Transitional Justice: The Function of Narrative in Redressing the Legacy of Mass Atrocities

2018· dissertation· en· W2955639230 on OpenAlexaboutno aff
Steven James Rita-Procter

Bibliographic record

VenueYorkSpace (York University) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsTransitional justiceRhetoricNarratologyRetributive justiceRestorative justicePolitical scienceLawIdeologyNarrativeEconomic JusticeSociologyCriminologyLiteratureArt
DOInot available

Abstract

fetched live from OpenAlex

This doctoral dissertation, Narratology, Rhetoric, and Transitional Justice: the Function of Narrative in Redressing the Legacy of Mass Atrocities, examines the extent to which the success and feasibility of human rights tribunals and truth commissions are dependent upon the ways in which the past is narrativized in State-sponsored legal reports and subsequently promulgated through the stories we tell. Juxtaposing three historical cases that have constituted transitional justice according to divergent ideological paths, Narratology, Rhetoric, and Transitional Justice compares and cross-references the final reports on three high-profile transitional justice cases: the Nuremberg tribunals (1945-49), the Argentine Trial of the Juntas (1985), and the Canadian Truth and Reconciliation Commission (2008-15), to study the ways in which these reports have shaped the collective or national memories of various historical traumas. The dissertation examines how the final reports on truth commissions and war crimes tribunals deploy a highly sophisticated set of rhetorical and narratological techniques in order to fix a single, specific version of historical events in the cultural memories with disparate aims in bringing together a fractured nation. By highlighting the significant degree of artistry that go into preparing these reports, it examines how and why transitional governments are often motivated to frame historical violence in order to elicit collective feelings of outrage, shame, guilt, or forgiveness. Narratology, Rhetoric, and Transitional Justice thereby illustrates how transitional justice practices mobilize blueprints for reconciliation, restoration, or retribution through the recovery and narrativization of traumatic memories, and how these respective sentiments have facilitated the implementation of subsequent political and economic policies by the transitional governments. A key aspect of this analysis centeres on the unique ability of final reports to contextualize national traumas by designating precisely which crimes were committed, by and against whom, by regulating whose testimony is to be included and/or excluded from the master narrative, and by articulating the appropriate measure of justice that ought to be faced by the perpetrators. As the apotheosis of the transitional justice process, my research demonstrates that truth commission reports not only present their mercurial and highly contentious histories as binding, legally-validated, and irrefutably fixed versions of a series of often dubious events, but they also effectively situate each citizen within the victim/perpetrator and innocent/guilty binary ethical paradigms upon which the judicial system is grounded. Negotiating the final reports on truth commissions and human rights tribunals as historical non-fiction texts, these case studies weigh their reports alongside other vehicles of cultural storytelling (including historical novels, films, ballets, etc.).

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)0.006
metaresearch head score (Gemma)0.011
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0090.031
Scholarly communication0.0120.011
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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