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Record W6941963255 · doi:10.14288/cl.vi247.192434

Discomforted Readers and Cultural Politics of Genre in Lawrence Hill’s The Illegal

2020· article· en· W6941963255 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeReading (process)PoliticsConversationAmbivalenceState (computer science)Popular culture

Abstract

fetched live from OpenAlex

The Illegal by Lawrence Hill was released September 2015, a particularly discomforting political moment when news of asylum seekers was clearing the front pages and debates about Canada's global responsibilities were determining a federal election. Because of its publication year, overlapping popular genres, and curious reception, The Illegal opens up a valuable conversation about the relationship between Canadian refugee fiction as popular pedagogy and contested imaginaries of the refugee figure within Canada's projections of a humanitarian national identity. The novel is a playful speculative political thriller that satirizes the hostility of the global community and the ambivalence of state humanitarianism. A number of readers and reviewers have expressed discomfort with the pairing of popular genre fiction with a refugee thematic. This article analyses the book's reception in online reviews and shared reading events, against a literary reading of the book through the lens of genre. It notes an interpretive gap and asks what cultural refugee studies can learn from this gap about humanitarian reading publics and Canadian refugee literature.

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)0.009
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: Empirical · Consensus signal: none
Teacher disagreement score0.528
Threshold uncertainty score0.949

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0340.033
Scholarly communication0.0210.005
Open science0.0010.005
Research integrity0.0030.005
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 head0.033
GPT teacher head0.247
Teacher spread0.213 · 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
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
Published2020
Admission routes1
Has abstractyes

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