Paradox and Trespass: Possibilities for Ethical Practice in Times of Austerity
Bibliographic record
Abstract
How social workers do construct what is ‘ethical’ in their work, especially when they are positioned at the intersection of multiple paradoxes, including that of two opposing responsibilities in society: namely, to care for others but also to prevent others from harm? Paradoxes in practice are especially complicated to manage in the neoliberalism of the Global North where the priority of efficiency has been heightened and the obligation towards the most vulnerable has been weakened. Taking data from a Canadian study, this paper delineates the structural paradoxes of practice and elaborates on the concept of ethical trespass, especially in times of austerity. Ideas are illustrated with case material from participants who were practitioners working with a population of young single impoverished mothers. Impacts of the discourses on single mothers, resource inadequacies, and increased surveillance on clients are discussed, and the effects on workers such as individualization and burnout will be outlined. Despite the challenges, practitioners in the study found ways to minimize trespass and act ethically. The strategies employed included problematizing the helping relationship, being a responsible traitor, dissident speech, and the use of contextualized practice.
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 | 0.069 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.029 | 0.208 |
| Scholarly communication | 0.029 | 0.033 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".