Bibliographic record
Abstract
We have heard a great deal about the reaction of Jewish people and Christians (particularly in America) to the catastrophic events of'9/11' in New York, when the twin tours of the WTC came tumbling down. But we have not heard much, if at all, of the response of Hindus in inJia and in the diaspora at large to these events. In this paper I take a slightly different route: I come to it from a more distant region, with which I am familiar, namely, from the quarter of Hindus settled in Australia, whose close links with the global diasporic Hindu network places them on the larger map as well. This connection is of particular interest here. The paper develops, via a narrative, a critical perspective on how a majoritarian religion from its own national situation (and cross-border tensions) responds to the events of September 11 and aligned fall-outs, to extend its wariness about a minority religious presence (though fearsomely more than a small 'minority' in Indonesia) across the globe. This case study might be exemplary for a study of immigrant South Asian religious communities in other regions, such as Europe as well.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".