Enhancing critical social work practice: Using text-based vignettes in qualitative research
Bibliographic record
Abstract
There exist ongoing calls among social work scholars and practitioners to cultivate applied knowledge of critical and emancipatory practice. In this paper, I explore the utility of text-based vignettes as instruments that can be used to elicit insight from marginalized service users on critical social work practice. To do this work, I draw on data from interviews with 20 transgender and gender diverse (TGD) social service users, along with 10 social workers, whose responses to a text-based vignette were originally used to build an understanding of the constituents of equitable social work practice with TGD people. Incorporating critical pragmatism as a conceptual framework and constructivist grounded theory as a methodological orientation, I analyze data from this study as an exemplar that substantiates the promise of using text-based vignettes in qualitative social work research to generate knowledge of critical social work practice. Specifically, I demonstrate how text-based vignettes in this study (1) contextualized the meaning, significance, and impact of oppression for service users, (2) built insight on practice that reflects solidarity and allyship, and (3) identified opportunities for social workers’ reflexive use of professional power to effect change. Accounting for the tensions between empiricism and critical praxis in social work, I consider the promise of incorporating text-based vignettes to develop empirical social work literature that is rooted in the voices of marginalized service users.
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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 | 0.067 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".