Environmental ethics for social work: Social work's responsibility to the non‐human world
Bibliographic record
Abstract
Gray M, Coates J. Environmental ethics for social work: Social work's responsibility to the non‐human world This lead article in this Special Issue begins discussion on an environmental ethics for social work and raises arguments as to whether and, if so, why social workers have duties, obligations, responsibilities and commitments to the non‐human world. It provides an overview of the field of environmental ethics in searching for a moral stance to affirm an environmental social work. To what extent should social workers engage in fundamental geopolitical issues concerned with climate change, global warming, environmental degradation, pollution, chemical contamination, sustainable agriculture, disaster management, pet therapy, wilderness protection and so on and, if so, why and how? Are these issues incidental and peripheral and only of concern when they impact upon humans or do social workers have a responsibility beyond human interests? What is the significance of the ‘non‐human’ for social work? The article explores the terrain of the burgeoning field of environmental ethics to determine whether convincing ethical grounds for environmental social work might be found beyond hortatory claims of what the profession ought to be doing to address environmental concerns.
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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".