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Record W2408918915 · doi:10.1075/etc.8.2.01bel

Saying the unsayable

2015· article· en· W2408918915 on OpenAlexaff
Valérie-Anne Belleflamme

Bibliographic record

VenueEnglish Text Construction · 2015
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsCBC (Canada)
Fundersnot available
KeywordsNarrativeReflexivityReading (process)WishAestheticsSociologyDimension (graph theory)EpistemologyLiteraturePhilosophyArtLinguisticsSocial scienceAnthropology

Abstract

fetched live from OpenAlex

In her novel Sorry (2007), Australian novelist and essayist Gail Jones engages in a reflection on the ethics of reconciliation. Written in response to her wish to acknowledge the debt to the Stolen Generations, Sorry offers new possibilities of ethical mourning, allowing the dead to return and the voiceless to speak. This article explores the ways in which Jones not only fashions a narrative that bypasses the unsayable dimension of Australia’s history and the representational difficulties inherent in trauma but also fosters the empathetic imagination through a metadiscursive discussion of the act of reading. Self-referentiality and self-reflexivity are also examined, as they allow Jones to draw attention to her novel’s writerly elaborations and offer an alternative to standard reconciliation practices.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0110.024
Scholarly communication0.0080.009
Open science0.0010.006
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.040
GPT teacher head0.288
Teacher spread0.249 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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