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Record W4233287814 · doi:10.1017/9781108850087.017

Canadian Routes

2020· book-chapter· en· W4233287814 on OpenAlexaboutno aff
Michael A. Bucknor

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typebook-chapter
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaContext (archaeology)Period (music)Government (linguistics)HistoryPower (physics)GeographyAfrican descentGenealogyGender studiesPolitical scienceSociologyArchaeologyArt

Abstract

fetched live from OpenAlex

Migration to England has been a better-known context for the development of Caribbean writing, but migration to Canada also shaped the region's literary history during this period. The Windrush generation that went to England in the 1950s interacted in important ways with Canada; on the one hand, there was a parallel migration of Caribbean people and writers to Canada during this period, while on the other hand, many of the writers who first went to England visited Canada and some later relocated to Canada. This migratory experience complicated the idea of Caribbean writing as responding primarily to a British metropole and offered different ways of thinking about blackness, exile, diaspora, and belonging. Although it is Toronto that has become the major hub for Caribbean artistic activity in Canada, Montréal was an important meeting point during this period and in 1968 was the venue for the Congress of Black Writers which brought together luminaries, writers, and activists from the region, the USA, and Africa in the context of the Black Power movement. The Caribbean demographic in Canada was affected in the 1950s and 1960s by the Canadian government's recruitment of officially 'single' women as domestic workers and later by significant numbers of Caribbean nationals of Indian as well as African descent. An important issue is how the migratory demographic affected the writing during the period.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.797
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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.166
Teacher spread0.138 · 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
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

Citations1
Published2020
Admission routes1
Has abstractyes

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