Social workers’ peculiar contribution to ethnographic research
Bibliographic record
Abstract
Over generations, social workers have borrowed theories from sociology. However, sociologists have generally avoided borrowing theory from social work. By beginning with social work practice wisdom, we can unfold the complex elements organizing social work practice and by extension ethnographic research. Complexity and resulting uncertainty are antidotes for theoretical purity. Practice as grounded in life, that of client’s and social workers is inherently “dirty”, i.e., messy, disorganized, confusing, unfolding, and uncertain. Understandings and practices are accomplished in a connection of self to a profession, agency/organization, mandate and purpose, and ethical orientation, in interaction with colleagues and clients. Social workers take sides as they are grounded in an ethic of care. The challenge of developing an ethical practice in the face of difference, disagreement, disjunction, and conflict lead social workers to bracket, and hence reflect on the putative coherence of a “life world.” Face-to-face work with individuals rather than being a liability provides a source of knowledge and wisdom to inform social science generally.
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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 | 0.082 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".