Algorithmic management and control at work in a manufacturing sector: Workplace regime, union power and shopfloor conflict over digitalisation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract It is often stated that algorithmic management disrupts control regimes and enables employers to dictate the work effort level. This article argues that control at work must be conceived through inherent tensions in employment relations and contradictions that result from the implementation of technologies in workplaces. Building analytically on two theoretical approaches (workplace regimes and power resources), conflicts over algorithmic management on the shopfloor are conceptualised through structural characteristics of workplaces and strategic factors related to workers' power. To illustrate these tensions, qualitative data is mobilised from a case study of the aluminium industry in Québec (Canada), where algorithmic management was implemented to advance efficiency and intensify work. The main contribution of this article is to highlight the persistence of an ‘embedded control regime,’ which we explain through the structural characteristics of the sector under study (technology, production, and market) and the power resources mobilised by workers and unions. This study advances knowledge of the deployment of algorithmic management beyond the ‘gig economy’ by exploring the avenues through which workers and unions can effectively contest such technologies in the workplace.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it