“Walking the talk”: Putting environmental social work theory into practice
Bibliographic record
Abstract
Abstract This study explored how environmental social work is done in practice, looking at those who are already doing this work. Six social workers who integrate land and place in their practice in the Northwest Territories (NWT), Canada, participated in a reflective outdoor activity followed by an in‐depth interview. Five themes emerged from the data, including Lessons from the Land, Lessons in Ways of Being for Social Workers, Lessons from Being in Relationship, Lessons for our Workplaces and Profession, and Lessons in Practice. This article offers practical examples for social workers and other service providers seeking to connect their practice to the land; it also contributes practice wisdom to emerging discussions regarding both what can be done in environmental social work practice and how.
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.
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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.013 | 0.069 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| 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".