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Record W375503903

How to Cook a Good Tale - Three Stories from Rohinton Mistry's Swimming Lessons and Other Stories from Firozsha Baag (1987)

2002· article· en· W375503903 on OpenAlexaboutno aff
エンゲルベルト ヨリッセン

Bibliographic record

VenueKyoto University Research Information Repository (Kyoto University) · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeHistoryArtVisual artsLiterature
DOInot available

Abstract

fetched live from OpenAlex

On the following pages I want to concentrate on three stories from Rohinton Mistry's (1952 -) Swimming Lessons and Other Stories from Firoszha Baag (1987)*' The eleven stories of this collection are all placed in a Baag, or Baug, here in the meaning of "Parsi housing estate or 'colony"'*' in Bombay.They can be read independently from one another, however one of the charming points of the book is the fact that the different stories are intertwined by figures from certain stories which reappear in other ones.More important is the function of the concluding story in the book which combines them all.Without confusing elementary narratological principles, that is mixing up figures, narrators and the author himself, in this case it will be possible to say, that the stories in the collection are reflecting most presumably experiences by the author.Rohinton Mistry*' was born in a Parsi family in Bombay, in 1975 he emigrated to Canada, and studied at Toronto University.The main figure in the first story discussed here,The Ghost of Firozsha Baag, is a sixty-three-year old ayah ("nurse-maid", etc., L, p.55)* 4 in one ofthe Parsi households.She is a Catholic from Goa and this enables her to see the Parsis from outside, more exactly, as will be seen, from below, because a main part of her living takes place on the How to Cook a Good Tale -Three Stories from Rohinton Mistry's Swimming Lessons and Other Stories from Firozsha Baag ( 1987) "floor'' (p.45).This poses her into a picaro-like position, but picaro is used here of course only as a makeshift.In the second story Squatter the troubles of a young man from Firozsha Baag who emigrates to Canada are told.If not exactly parallel one can read this story as a mirror reflecting that of the ayah from Goa because now the Indian Parsi is in a position comparable to her's in the Parsi household.The narrator of this story being depicted in strange positions on the toilet can, thus, be seen too, at least in some moments, resembling a picaro with a queer position in society.The third story Swimming Lessons tells at the same time the story of one more emigrant from Firozsha Baag to Canada, his fixing down his memories in a book of short stories and his parents' reaction to this.The somewhat picaresque feature of the narrative character to be found especially in the two first mentioned stories and the concluding one is alluded there by the father when he expresses his feelings about Squatter, that the story "with a little bit about Toronto, where a man perches on top of the toilet, is shameful and disgusting, althoug it is funny at times and did make me laugh" (245-246)*s Just one word to the character of this paper, which belongs at the same time to two projects I am working on now.One of which concentrates on Indian literature in English, the other one on the problems involved in Goan identity.The first has, not intendedly from the beginning, brought up in three successive essays problems of Jews in India, and that meant, that the planed essay about the Parsi novelist R. Mistry could not stop at the level of writing about Indian authors writing in English, but should focus as well on the religious aspect.Pushing forward study on Mistry, there appeared his fascinating story about the How to Cook a Good Tale-Three Stories from Rohinton Mistry's Swimming Lessons and Other Stories from Firozsha Baag (1987) Catholic ayah from Goa in a Parsi household in Bombay, The Ghost of How to

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0010.003
Open science0.0010.001
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 head0.068
GPT teacher head0.279
Teacher spread0.211 · 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.

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

Citations0
Published2002
Admission routes1
Has abstractyes

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