Bibliographic record
Abstract
Summary How does one go about doing or engaging in ethnomethodological study of local occasions? Would such study be of value for social workers, hence would it help them to understand the everyday accomplishment of practice as social work? Harold Garfinkel, the founder of ethnomethodology, argued that the task is to start with and to be in the midst of ordinary and everyday activities. A beginning in ordinary, mundane, and everyday activities is also to be surrounded by taken-for-granted understandings, frameworks, and facts or facticities. The focus on “facticities” of everyday things directs us to attend to utterly ordinary and mundane interactions, and here there is deep congruence with social work interests and practices. Findings This paper turns to Garfinkel’s oeuvre to set out in readily understandable language the orientation and tools needed for social workers to do ethnomethodological studies. A focal question is: Just how might social workers in the midst of practice actually go about engaging in EM? Application By taking up tools from ethnomethodology, social workers can better understand and explicate the essential reflexivity of their everyday practice. As a result, EM provides a pathway for both understanding and teaching effective social work through a reflective and reflexive turn.
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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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".