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Record W4399859331 · doi:10.4324/9781003474005-3

Writing Ourselves into Time: Stories of Indo-Trinidadian Women

2024· book-chapter· en· W4399859331 on OpenAlexaboutno aff
Prabha Jerrybandan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCaribbean history, culture, and politics
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryLiteratureArt

Abstract

fetched live from OpenAlex

Three generations of Indo-Trinidadian women are the major subjects in the stories that emerge as the researcher/writer situates herself as an Indo-Caribbean Canadian. A traditionally patriarchal culture has previously rendered women as minor characters within the literature of the region. The writer gains new perspective after leaving a home country that has been characterised by colonisation. This work explores threads of memory in retrieving stories that are at risk of disappearing over time. Customs that characterised life for earlier generations of Indo-Trinidadian women show up as interesting details, giving insight into the resilience of people who had limited circumstances in rural Trinidad. As the generations progress, and as there is more urban exposure, identity is complicated by the dominant Trinidadian Creole culture. The stories offer glimpses into lives where traditional Indian culture morphs into a unique Indo-Trinidadian one. The three women embrace assimilation into a larger Trinidadian culture, but in varying degrees. For the researcher, learning to write about Indo-Trinidadian women’s experiences is as important as the stories that are written. Through interviews, questioning, authoethnography, memory work, and storytelling, events are reconstructed from snippets of inherited fragments of experiences.

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)0.002
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0180.015
Scholarly communication0.0060.005
Open science0.0020.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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