Bibliographic record
Abstract
Summary In today’s environment dominated by managerialism and fiscal restraint, actualizing the principle of social justice has become a daunting task for social workers. Supervision has been identified as a promising site for enacting social justice, but evidence is lacking that supervision conversations support socially just practice. A concurrent mixed model nested research design was used to explore the needs of social workers for supervision conversations about social justice and practice. A mixed method web-survey on supervision was completed by 636 social workers from a broad spectrum of social work practice settings and geographical locations in Ontario, Canada. Quantitative data and written responses from open-ended questions are presented as an integrated narrative. Findings The results demonstrate that social worker participants shared a need for supervisors to promote and provide space for conversations about multiple aspects of social justice and practice. This need for a social justice focus had not been currently or recently experienced by a significant number of participants who worked in a variety of settings. Applications In response to the findings and their inferences, implications for supervision knowledge, practice and policy development are provided that could help social workers better actualize social justice in their day-to-day practice.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".