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Record W7847015

""I don't get out without a fight"": exploring the life stories of Chilean Exiles

2005· dissertation· en· W7847015 on OpenAlexfundno aff
Matthew Scalena

Bibliographic record

VenueActa Cytologica · 2005
Typedissertation
Languageen
FieldSocial Sciences
TopicPolitical and Social Dynamics in Chile and Latin America
Canadian institutionsnot available
FundersSimon Fraser University
KeywordsNarrativePoliticsSeriousnessHistoryGender studiesSociologyHumanitiesLiteratureArtPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

This project draws upon interviews with members of the Chilean exile community in Winnipeg, focusing on the life stories of Jose and Veronica-a couple exiled in 1976.Jose's narrative of pre-exile life in Chile is typical of the dominant Chilean exile narrative.His story establishes his credibility as a political refugee, concentrating on Chilean politics and the seriousness of his political activity.Veronica, however, tells a very different account of life in Chile.Her narrative is characterized by teenage hijinks, detachment from Chile's socialist project, and excitement about moving to Canada.Her divergence from the dominant exile narrative is best understood through an exploration of her life as a young woman in Chile and her more recent Canadian experiences.Both are essential components to the way she remembers and narrates her life story.Read against Jose's contrasting narrative, Veronica's story sheds light on a profoundly different exile experience.iii I would like to thank primarily Veronica, who allowed me into her home and spent countless hours with me narrating and discussing her life.Without your incredible generosity, this life story would not have been possible.Along the same line, I thankfully acknowledge the time of all the other Chilean-Canadians with whom I have spoken over the past few years.All of our talks have bettered my understanding of the Chilean exile experience.My deepest gratitude goes to my supervisor, Dr. Alec Dawson, who has not only provided insight into the historical questions with which I have grappled over the past two years but has, more importantly, challenged me to rethink those very questions.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.021
Scholarly communication0.0070.005
Open science0.0010.008
Research integrity0.0020.005
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 head0.064
GPT teacher head0.345
Teacher spread0.282 · 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 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

Citations0
Published2005
Admission routes1
Has abstractyes

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