Ethnography as a methodological descriptor: the editors' reply
Bibliographic record
Abstract
Dr Jowsey worries that the use of the term ‘ethnographic’ in the title of a recent short report by Lamba et al 1 of a study of patient safety in team rounds risks watering down the meaning of ethnography as a research methodology. The data for the study were collected by one observer, who used a structured data collection form to record patient safety issues that were brought up or occurred during team rounds, as well as identifying any consequences and whether the issue was actionable. The observational data regarding issues were categorised using a set of thematic categories derived from the London Protocol, and were summarised using descriptive statistics. Jowsey notes that the Lamba et al 1 article identifies in its title and methods …
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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 | 0.043 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.051 | 0.070 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".