Overt and Covert Ways of Responding to Moral Injustices in Social Work Practice: Heroes and Mild-Mannered Social Work Bipeds
Bibliographic record
Abstract
This article explores overt and covert actions taken by social workers against perceived moral injustices in their work organisations. Covert and overt actions are defined and examples of these actions from a research study of social work ethics are presented. The paper argues that both covert and overt actions ought to be considered heroic in light of what appears to be timidity on the part of many social workers to act against perceived moral injustice in their workplaces. The concepts of multiple institutional logics and embedded agency are used as a means of moderating and contextualising the concerns social workers might have about acting in either covert or overt ways to address moral injustices, and to examine the potential pitfalls and merits of each type of action. The article concludes by encouraging social workers to consider more systematically avenues for overt actions to address perceived moral injustice, as basic social work values of client care can be paradoxically found even in the logics of dominantly neo-liberal organisations. If overt action is not possible or may have the potential of causing more harm to the client, covert actions can be morally justified.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.080 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".