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Record W2097283496

«Yet families are more than gene pools: their stories travel through and map us,too»: Janice Kulyk Keefer’s Honey and Ashes: A Story of Family

2009· article· en· W2097283496 on OpenAlexaboutno aff
Eleonora Rao

Bibliographic record

VenueUniSa. Sistema Bibliotecario di Ateneo · 2009
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsnot available
Fundersnot available
KeywordsGrandparentHomelandMemoirNarrativeGenealogyGender studiesHistoryExtended familyHollywoodBeautyFamily memberSociologyArtArt historyPolitical scienceLawAestheticsLiterature
DOInot available

Abstract

fetched live from OpenAlex

Honey and Ashes () revolves around a quest for identity, national and personal; more precisely around a quest for lost origins. In keeping its focus throughout the narrative on family snapshots and studio portraits this family memoir of Ukrainian Canadian author Janice Kulyk Keefer foregrounds the role of photographs in recovering and reconstructing memories of a lost past. Keefer was born in Canada, but her parents and grandparents have a history of painful displacement from their mother country, a place she experienced vicariously through the memories of her relatives, her mother’s above all. The photos help her to give concrete evidence of their story, from which she was, in actual fact, excluded. In Honey and Ashes Janice Kulyk Kefeer explores the issues that haunt those who live simultaneously in two countries: the country of the heart and that of the mind. The author was born in Canada, a country which for her family and herself meant freedom and a future filled with opportunities. She was also born, however, into the history of her family’s homeland, Ukraine, thus inheriting both the gift and the burden of her family’s homeland past, a past that is perceived as «an equal spill of beauty and blood». The painful, disconcerting aspects of this process of remembering are remarked upon throughout the text:

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.052
GPT teacher head0.297
Teacher spread0.245 · 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 designObservational
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
Published2009
Admission routes1
Has abstractyes

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