The Lateralization Method and Thomas A. Tweed’s Theory of Religion: Āyurveda and Hindu Migrants as Case Study
Bibliographic record
Abstract
What is offered here is first a description of the question-generating method that I call lateralization. I argue that this method is helpful to researchers studying religion among individuals and groups, especially researchers in the initial stages of a given qualitative research project. Second, I argue that the lateralization method is particularly effective for scholars of transnationalism, diaspora, and migration when applied to Thomas A. Tweed’s theory of religion. Acknowledging religion as a significant force for diasporic communities as they rebuild and maintain identities, in his Crossing and Dwelling: A Theory of Religion (2006), Tweed posits that “[r]eligions are confluences of organic-cultural flows that intensify joy and confront suffering by drawing on human and suprahuman forces to make homes and cross boundaries.” Philip Crang, Claire Dwyer, and Peter Jackson (2003) have argued for approaching transnationalism by means of commodity culture to mitigate the dominant focus on an abstract transnationalism and on bounded diasporic communities. This article suggests that Tweed’s work on religion—modified by applying the lateralization method—would enable scholars to accomplish the tasks Crang, Dwyer, and Jackson have argued for, and would offer more to scholars interested in uncovering multiple dimensions of transnationalism that take religion into account. In the third part of this article, I will illustrate the application of the lateralization method in my own research. In this section, I show how focusing on Ayurveda as a lateralization into Tweed’s theory of religion is a way of engaging with Hindu migrants’ embodied experiences in diasporic spaces. The reader is invited to consider this question-generating lateralization method applied to Tweed’s theory for creative exploration in qualitative research projects.
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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 | 0.030 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".