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Record W4394940760 · doi:10.33137/ic.v37i1.42112

Tracking the Trajectories of Genni Gunn’s Writing

2023· article· en· W4394940760 on OpenAlexvenueaboutno aff
Deborah Saidero

Bibliographic record

VenueItalian Canadiana · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTracking (education)Gunn diodeComputer scienceArtificial intelligencePsychologyEngineeringElectrical engineeringPedagogy

Abstract

fetched live from OpenAlex

This article examines Genni Gunn’s writing in light of her childhood immigration experience and contends that, despite the crypto-ethnic approach she has adopted vis-à-vis her Italian-Canadian heritage, migration figures prominently in both her fictional and personal works, which bear traces of her autobiographical vicissitudes and reflect her own process of coming to terms with trauma. Indeed, her novels, stories, poems, and travel accounts all develop along shared trajectories, which include not only the obsession with travel, movement, and memory, or the search for identity and interpersonal ties, but also the focus on loss, grief, and abandonment, which are typical psychopathologies of migration trauma. Ultimately, Gunn embraces creative writing as a site of healing from the loss and pain endured as an effect of separation, abandonment, and migration, which are investigated beyond identification with a specific ethno-cultural group to encompass universal human experiences of trauma.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
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 head0.041
GPT teacher head0.265
Teacher spread0.224 · 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
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

Citations0
Published2023
Admission routes2
Has abstractyes

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