Australian qualitative insights from an international project on environmental practice in social work
Bibliographic record
Abstract
The environmental crisis continues to create new challenges for social workers and their clients and this has been recognised in the academic literature. For example, authors have highlighted the increasing impact of climate change, a need to advocate for environmental justice, and to reassert a sense of ecological justice. While academic attention has increased, it is more important to understand the practice of social workers in the field. This paper reports on components of an international project, focusing on the qualitative data collected through a national survey of Australian social work and human service professionals that explored their perspectives regarding the natural environment and climate change. The paper focuses on insights gained from a thematic analysis of the qualitative responses using NVivo data management software and reports in relation to the micro, meso and macro levels of practice. It discusses barriers and facilitators to expanding environmental practice in workplaces and provides suggestions for expanding social worker education that will support them to embrace and promote environmental practice. Results confirm ideas already represented in the literature, however also propose new directions in research and practice in the field at local, regional and global levels.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".