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Record W2773157479 · doi:10.14288/cl.v0i232.189456

On Refugees, Running, and the Politics of Writing: An Interview with Lawrence Hill

2017· article· en· W2773157479 on OpenAlexaffabout
Laura Moss, Brendan McCormack, Lucía Lorenzi

Bibliographic record

VenueOpen Collections · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPoliticsConversationPower (physics)RefugeeContext (archaeology)HistoryMedia studiesSociologyGender studiesRacismPolitical scienceLaw

Abstract

fetched live from OpenAlex

A former journalist and political speechwriter, Lawrence Hill has published ten books of fiction and non-fiction. The impact of his work as a novelist, essayist, memoirist, activist, and educator speaks to the power of writing to effect social change. “Artists have voices,” he affirms in the interview below, “and their voices can help influence—profoundly, sometimes—the way we see ourselves, and the way we see our country and the world and our roles in them.” Hill’s voice has contributed widely to pressing conversations about race, Black history, and social justice in North America for over two decades. Laura Moss, Brendan McCormack, and Lucia Lorenzi joined Hill to discuss his most recent novel, The Illegal (2015), which explores the contemporary refugee crisis in a global context, as part of a larger conversation about the conjunction of art and politics in Hill’s work as an author, public intellectual, and prominent voice within the Canadian literary community.

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)0.007
metaresearch head score (Gemma)0.012
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 score0.252
Threshold uncertainty score0.506

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0670.029
Scholarly communication0.0130.008
Open science0.0030.005
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0050.001

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 head0.032
GPT teacher head0.311
Teacher spread0.279 · 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

Citations3
Published2017
Admission routes2
Has abstractyes

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Same venueOpen CollectionsSame topicCanadian Identity and HistoryFrench-language works237,207