Coercive Care: Control and Coercion in the Restructured Care Workplace
Bibliographic record
Abstract
Abstract This article re-analyses 105 interviews from four qualitative research studies of different kinds of care workers in Canada and Australia, in light of deepening workplace rationalisation and austerity. The sample included: two studies of voluntary sector social service and social workers—one study of nineteen and one of twenty social workers, twenty-two telenurses and forty-four nursing home care workers. Themes that emerged from the data included: restructured and rationalised workplaces, managerialism, reshaped practice, unintended neglect and control of service users, coercion of workers and resistance strategies. Significant similarities were apparent across these similar and yet different care workers, suggesting a strong convergence of working conditions and the policies shaping them. The article used Labour Process Theory and returned to state theory debates in order to re-assess how rationalised care workplaces shape relations between care workers and the individuals they care for. The article contributes to state theory by suggesting a control-coercion care continuum and to Labour Process Theory by suggesting three kinds of workplace control/coercion: control through compliance, cutbacks coercion and contextual coercion. The findings draw attention to deep tensions within increasingly managerialised, austere state-run and state-funded care services in which control and coercion intermingle with resistance and care.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".