The Computer Social Worker: Regulatory practices, regulated bodies and science
Bibliographic record
Abstract
Social work assessments, and in turn clinical judgment and intervention practices, are increasingly framed by standardised tools and technologies that are digitised. These tools and technologies mediate social workers’ relationships with services users, while also privileging, and in turn reiterating, particular identities and particular forms of knowledge. In this article, I am interested in how standardised tools and technologies, like computers, operate to mediate the relationship between social workers and services users. I work with an autoethnographic narrative in order to examine standardised social work practice. Methodologically, autoethnography rests within a reflexive frame of qualitative research, allowing us to excavate our experiences in order to understand how our lives are ordered and knowledge is socially constitutive. In mining this narrative, I am interested in the body, and in particular, the corporeal dimension of standardised practices. I historically locate these practices, and use the work of Michel de Certeau and Michel Foucault to examine how tools and technologies function in relation to the body, even when there is no direct physical, bodily contact. Ultimately I argue that there is a scientific discourse underpinning current clinical practice and I use the framings of Donna Haraway to understand the implications of this for social workers.
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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 | 0.037 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.097 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".