How to convey social workers’ <i>understanding</i> to clients in everyday interactions? Toward epistemic justice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A client’s experience of being heard and understood by a social worker contributes to not only fostering alliance and positive outcomes in practice, but also positioning the client as the knower of their own experience. One’s capacity to be recognized as a trustworthy conveyer of knowledge is essential to achieving a sense of human value. Philosopher Miranda Fricker describes this recognition as epistemic justice. When the client’s experiences are discredited/marginalized, epistemic injustice occurs. We apply these constructs to clinical social work practice to explore how social workers accomplish or fail at the task of empathically listening and conveying understanding to clients and subsequently, accomplishing or failing epistemic justice. Using Conversation Analysis, we conduct a turn-by-turn analysis of videotaped social work encounters in a community mental health agency. Our findings identify and illustrate the moment-by-moment discursive patterns in both epistemically just and unjust clinical social work encounters. The identified discursive patterns can be used for the clinical training of social workers, encouraging critical reflection on their own everyday conversations toward promoting epistemic justice. This study thus has implications for social work education as an illustrative example of making a link between micro practice skills and macro justice issues in social work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it