An Ethnographic Toolkit for Studying the Networking Pathways of Hard-to-Reach Populations: The Case of Cosmetic Surgery Consumers in South Korea
Bibliographic record
Abstract
This article develops a novel ethnographic toolkit for examining the networking pathways that hard-to-reach populations use to socially survive. The toolkit consists of two sampling strategies (snowball and purposive sampling) and three data collection practices (role shuttling, site shuttling, and autoethnography). This article illustrates the applications of the toolkit in an ethnography of South Korean cosmetic surgery clinics and digital forums from 2018 to 2019 by uncovering the role that furtive networks play in facilitating cosmetic surgery consumption. Longitudinal in nature, the toolkit excels in examining the network’s dynamism, informal hierarchy, and the meaning-making and networking pathways that allow members of a hard-to-reach population like cosmetic surgery consumers in South Korea to participate in stigmatized practices. In the hard-to-reach population of surgery enthusiasts, I find that surgery is purchased by consumers through persuasive reconstructions of the meanings of success, body, and self by an elusive network of clinicians, who are introduced by an ever-changing roster of past cosmetic surgery consumers perceived to be high-status.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".