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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".