Bibliographic record
Abstract
This study addresses the growing call from scholars, such as Heidi Campbell, for a deeper reflection of methodological approaches to digital ethnography within various religious traditions and communities. In this article, I examine how I utilize a collection of “skillful means” informed by Buddhism, namely a mixed set of digital research methods encompassing reflexive choices and decisions, positioning, and creativities. This set of tools is situationally tailored for and derived from interacting with Chinese Buddhist diasporas in French Canada in the context of digital social media throughout my digital fieldwork. I use ethnographic vignettes to illustrate how these practices, afforded by the Buddhist ideas, digital possibilities, and ethnographic reflexivity, are crucial to constantly navigate, negotiate, and devise new strategies for exploring diverse networked digital field sites through interconnectivity, fluidity, immediacy and disruption and conducting multi-modal participant observation. By presenting the complexity and intricacy of the insider-outsider conundrum, I highlight key digital features of social media platforms such as WeChat, which can be strategically leveraged by a Buddhist researcher and practitioner to actively shape and present their digital image and voice within the communities they studies. I further reflect on how these dynamics can uniquely influence both the individuals and the communities being researched. Finally, I address the caveats and potential pitfalls this approach could potentially bring about.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".