MétaCan
Menu
Back to cohort
Record W3215608970 · doi:10.1163/18757421-05201002

Afrasian Aesthetics in M.G. Vassanji’s The Magic of Saida

2021· article· en· W3215608970 on OpenAlexaboutno aff
J.K.S. Makokha

Bibliographic record

VenueMatatu · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMAGIC (telescope)Identity (music)History of AfricaGender studiesAmbiguityHistoryDar es salaamContext (archaeology)TanzaniaAnthropologySociologyEthnologyArtAestheticsPhilosophyLinguisticsArchaeology

Abstract

fetched live from OpenAlex

Abstract The Magic of Saida by M.G. Vassanji (2012) centres on the central figure of the novel’s story, Kamal. He is the son of an African mother and an Asian (read Indian) father, who grows up in Tanzania and then relocates to Canada where he becomes an established doctor. The novel tackles themes of African-Asian (read Afrasian) racial identity, belonging, and the effects of the past on the present. Kamal identifies mainly as an African when residing with his mother in Kilwa during his childhood; he is then urged to embrace an Indian identity when he is sent to live with his uncle in Dar es Salaam in his early adolescence. Decades after moving to Edmonton, Canada, Kamal decides to come back to Kilwa. This paper explores the tension and ambiguity in Kamal’s identity by analyzing the way he defines himself—or is defined—in Kilwa and Dar es Salaam, and then investigating, through an eclectic psychochriticism lens, how that in turn affects him as he ages and drives him to return in seach of what it means to be both an Asian and an African in the context of East African cultural landscapes.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.019
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.209
Teacher spread0.188 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueMatatuSame topicPostcolonial and Cultural Literary StudiesFrench-language works237,207