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Record W4391121544 · doi:10.31436/asiatic.v10i1.749

Legacies of War in Current Diasporic Sri Lankan Women’s Writing

2016· article· en· W4391121544 on OpenAlexaboutno aff

Bibliographic record

VenueAsiatic IIUM Journal of English Language and Literature · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaHomelandGender studiesHistoryTheme (computing)Sri lankaEthnic groupSpanish Civil WarQuarter (Canadian coin)MulticulturalismMedia studiesPolitical scienceSociologyAncient historySouth asiaPoliticsAnthropologyLawArchaeology

Abstract

fetched live from OpenAlex

Since the end of the Sri Lankan ethnic conflict, Sri Lankan writers have sought to come to terms with the long-running war and its violent conclusion. This essay considers three recent novels by Sri Lankan diasporic women: Nayomi Munaweera’s Island of a Thousand Mirrors (2012), Chandani Lokugé’ s Softly, As I Leave You (2012) and Minoli Salgado’s A Little Dust on the Eyes (2014). Each of these novels focuses on the trauma of the war and the way that the war has affected and continues to affect those in the diaspora as well as in the homeland. Moreover, the novels provide a comparative view of the diaspora’s relation to the war, as Munaweera is resident in North America, Salgado in the United Kingdom, and Lokugé in Australia. In keeping with this issue’s theme – “from compressed worlds to open spaces” – my essay explores how South Asian women writers address the Sri Lankan war in the open spaces of the transnational Sri Lankan diaspora. As all three novels suggest, the end of the military conflict has not ended the need to understand the quarter-century of violence that preceded it. Diasporic women writers continue to intervene in a still fraught ethnopolitical situation, as all three novels deal with questions of loss, violence, trauma and the persistence of the conflict in the diaspora.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.278
Teacher spread0.270 · 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.

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

Citations2
Published2016
Admission routes1
Has abstractyes

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