Liz Przybylski. 2020. <i>Hybrid Ethnography: Online, Offline, and In Between</i>. SAGE Publications, Inc.
Bibliographic record
Abstract
Abstract As social media proliferates globally, affecting over half the world's population, ethnographic research must adapt to evolving modes of communication and representation. Liz Przybylski's book Hybrid Ethnography: Online, Offline, and In Between offers an accessible, practical guide to hybrid ethnography spanning both digital and physical spaces. Covering project formulation, research ethics, site selection, data collection, analysis, and writing, the book draws on the author's experience studying hip‐hop culture across the United States and Canada. Key strengths highlighted include the continuous focus on ethical considerations and the book's utility for researchers at all stages. The modular chapter design also allows for targeted consultation by researchers. Overall, this timely volume serves as an essential, durable guide for ethnographers navigating an increasingly digitized social landscape where subjects have greater control over self‐representation. It receives an enthusiastic recommendation for students and scholars alike.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".