Algorithmically Managing Risk and the Risk of Managing Algorithms in Australian Homecare: A Managerial Perspective
Bibliographic record
Abstract
This article examines how algorithmic management (re)shapes managerial understandings of risk within homecare. Engaging with the sociology of risk and care theory, we extend algorithmic management debates by examining its application in homebased disability and aged care. Drawing upon interviews with senior managers ( n = 15) from Australian homecare organisations, we explore how algorithmic management influences how risk is recognised and responsibilised. A notable difference of algorithmic management in homecare compared with other sectors is that a primary use for these systems is to support compliance and reduce harm, with the management of risks framed as a key care practice for managers. Our findings reveal that in homecare these systems, from a care perspective, enable organisations to operationalise risk, yet at the same time, institutionalise new managerial rationalities that both recalibrate and complicate care oversight. Finally, our findings highlight the importance of regulatory pressures in shaping how algorithmic management is used.
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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 | 0.032 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".