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Record W3034911674 · doi:10.1080/23277408.2020.1767985

Teaching Canadian Literature in the University of Nairobi

2020· article· en· W3034911674 on OpenAlexaboutno aff
Muchugu D.H Kiiru

Bibliographic record

VenueEastern African Literary and Cultural Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsKenyaSyllabusPedagogyFidelitySociologyCanadian literaturePolitical scienceEngineeringLawArt

Abstract

fetched live from OpenAlex

ABSTRACT This paper discusses experiences of teaching Canadian literature in the Department of Literature, University of Nairobi, Kenya. For a long time, the University of Nairobi syllabus required that teaching Canadian literature, or any foreign literature for that matter, should be related to an African experience. This paper explores a few possible but unworkable ways on how one could relate teaching Canadian literature to an African experience as a way of establishing its relationship to students in Nairobi, Kenya, which is over 10 000 kilometres away from Canada. On the whole, the paper argues that an effective approach to teaching the literature has been formalist that takes into account fidelity to the nature and function of literature, even as it reveals the universal in the particular that makes the form and content of Canadian literature relevant to Kenyan as well as to African experiences. In the end, the paper points out the past experience of teaching and learning Canadian literature must have been valuable to both student and teacher as two seminars held outside the university demonstrate that Kenyans can relate to it for — after all — literature is universal in the particular.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.604

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 head0.020
GPT teacher head0.224
Teacher spread0.204 · 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.

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

Citations2
Published2020
Admission routes1
Has abstractyes

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