Book Review - Teaching and Learning in Ecosocial Work: Concepts, Methods and Practice, Catherine Forde, Satu Ranta-Tyrkkö, Pieter Lievens, Komalsingh Rambaree, Helena Belchior-Rocha (eds)
Bibliographic record
Abstract
This publication frames the global issues of climate change and presents the role of social work and its professional obligations in a well-balanced and informed manner. At times, the reality of the issues involved can feel overwhelming and initially seem hopeless. However, this book proposes ideas and practices that enable the reader to view the identified concerns with cautious optimism. It offers a variety of plans for action (an essential ingredient for most social workers) that support the reader to embrace the book’s resounding and unequivocal call to action. In addition, the authors suggest numerous theoretical frameworks with terminology that explain and explore ecosocial work (and its various definitions), establishing the requirement to extend the scope of our understanding beyond established ‘person in environment’ theory to one that encompasses the natural and non-human world. Thus, robustly confirming that these two entities are essentially connected and highlighting that survival is based on the survival of ‘all’ rather than just human beings. What is refreshing is that the enormity of the concern is not diminished but explored and examined critically and effectively offering concepts such as ‘hyperobjects’ and ‘eco anxiety’ which allow for an understanding of the overwhelm and paralysis felt by many who feel the issues are too large to comprehend, let alone somehow address. The case studies and practices that identify projects that have been undertaken to mitigate the concerns and the requirement to act, offer practical ways to address the issue and could be replicated and/or adapted to meet system, issue and/or country-specific needs.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.019 |
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".