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Record W2172814001 · doi:10.3138/utq.84.4.07

On Responsible Distance: An Interview with R. Cheran by Aparna Halpé

2015· article· en· W2172814001 on OpenAlexaffvenueabout
R. Cheran, Aparna Halpé

Bibliographic record

VenueUniversity of Toronto Quarterly · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsCentennial CollegeUniversity of Windsor
Fundersnot available
KeywordsTamilMilitantDiasporaContext (archaeology)NationalismDemocracyPoliticsSociologyCensorshipMedia studiesSri lankaGender studiesHistoryPolitical scienceLawAnthropologyLiteratureArtArchaeologySouth asia

Abstract

fetched live from OpenAlex

R. Cheran speaks on his trajectory as a Tamil poet, journalist, and intellectual, during the years of conflict in Sri Lanka and on his current work as a playwright, activist, and collaborator in the development of Tamil diaspora studies with Chelva Kanaganayakam in Toronto, Canada. This interview provides a glimpse of the histories of dislocation, censorship, and exile that framed Tamil political, cultural, and intellectual life throughout the latter half of the twentieth century and the beginning of the twenty-first century. Precariously positioned as an artist and scholar who eschewed the non-democratic, militant positions of successive Sri Lankan governments and Tamil militant organizations, Cheran interrogates evolving notions of Tamil nationalism as articulated in the post-war context and looks to the future of the idea of the Tamil nation in Sri Lanka and around the world. This interview is a transcript of the public interview held at Trans(sub)continental Imaginations: Three Centuries of South Asian Literary English, a symposium in memory of Chelva Kanaganayakam, University of Toronto at Mississauga, 25 March 2015.

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)0.004
metaresearch head score (Gemma)0.008
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: Other · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0250.007
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0030.010
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 head0.022
GPT teacher head0.201
Teacher spread0.179 · 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
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

Citations4
Published2015
Admission routes3
Has abstractyes

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