Qualitative research methodologies: ethnography
Bibliographic record
Abstract
The previous articles (there were 2 before this 1) in this series discussed several methodological approaches commonly used by qualitative researchers in the health professions. This article focuses on another important qualitative methodology: ethnography. It provides background for those who will encounter this methodology in their reading rather than instructions for carrying out such research. Ethnography is the study of social interactions, behaviours, and perceptions that occur within groups, teams, organisations, and communities. Its roots can be traced back to anthropological studies of small, rural (and often remote) societies that were undertaken in the early 1900s, when researchers such as Bronislaw Malinowski and Alfred Radcliffe-Brown participated in these societies over long periods and documented their social arrangements and belief systems. This approach was later adopted by members of the Chicago School of Sociology (for example, Everett Hughes, Robert Park, Louis Wirth) and applied to a variety of urban settings in their studies of social life. The central aim of ethnography is to provide rich, holistic insights into people’s views and actions, as well as the nature (that is, sights, sounds) of the location they inhabit, through the collection of detailed observations and interviews. As Hammersley states, “The task [of ethnographers] is to document the culture, the perspectives and practices, of the people in these settings. The aim is to ‘get inside’ the way each group of people sees the world.”1 Box 1 outlines the key features of ethnographic research. #### Box 1 Key features of ethnographic research2
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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 | 0.083 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".