Bibliographic record
Abstract
A cavaranserai was an inn where travelling Muslim merchants would gatherat night to relax after a hard day’s journey, share meals, and tell stories toeach other. These themes of travelling and storytelling set the scene forHanifa Deen’s wonderful book about these people, who, originally travellersthemselves, arrived on the continent around the eighteenth century.Moreover, the book is a story of Deen’s journey around Australia to collectthe stories of her fellow Muslim compatriots.Caravanserai was originally published in 1995. The impetus behindthe book was Deen’s sense during the first Gulf War (1991) that Muslimsin Australia did not have a human face – they were known by the generalpublic only through negative stereotypes. She sought to tell some of theirstories to show that Muslims, just like any other group, were human beings who “mow their lawns, are preoccupied with losing weight, worryabout their jobs and mortgages, play sport, swap jokes or tell their childrenbedtime stories” (p. 8). She set out across Australia to collect theirstories.At the time, Deen found that Muslims were making their way inAustralia, becoming more accepted by the wider community and establishedas one of many others in Australia’s multiethnic, multireligious society.The 9/11 tragedy changed all that, and Muslims in Australia, as in otherwestern countries, found themselves treated as “enemy aliens.” Believingthat the clock had been set back, the author felt an urgent need to retraceher steps to find out how her country’s Muslim communities were faring.The result of the second journey appears as part 4, and its three long chaptersmake up nearly one-third of the book.Deen writes that she was asked time and again what kind of book shewas writing and, surprisingly, found that answering this question wasrather difficult. As she travelled, met people, and collected their stories, thestyle of Caravanserai emerged: part storytelling and part commentary.This combination has served her well, for her renditions of her interviewees’stories are beautifully written. She describes the people she meets, thescene and ambiance of their meeting, and her thoughts and emotions as sheretells their stories. She writes so well that I often felt that I was in theroom with her, interacting with the people around her. This was all themore poignant for me, since I am an Australian from Perth, like her, butwho became Muslim only after emigrating to Canada. Deen’s stories connectedme with the Muslim community in Australia that I have neverknown ...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".