Implementing automation: a study of the shopfloor politics of technology change in the Canadian aerospace sector
Bibliographic record
Abstract
This study examines how varying managerial and trade union strategies shape different social patterns of workplace technological change through a comparative study in the Canadian aerospace sector. Recent advances in technologies such as internet enabled devices, data storage, advanced robotics, and additive manufacturing, among others, have spurred a renewed interest in technology change in the workplace among social scientists. Previous research demonstrates that a key moment in workplace technological change occurs in the implementation and debugging phase, when workplace actors negotiate how a technology will be deployed on the shopfloor. Despite many studies in the labour process and industrial relations traditions examining the implementation of new technologies on the shopfloor, a theoretical framework for grasping the social patterns of debugging has remained lacking. This thesis develops such a framework through the comparative study of four technological changes at two factories operated by a Canadian aerospace firm and thus deepens our understanding of how workplace actors can shape the trajectories of technological change. \nI explain the observed variations in patterns of implementation through an examination of actor strategies. Here, managerial strategies are defined as a relationship between forcing and fostering while trade union strategies are categorised according to the presence or absence of a considered, timely, and organised union response. Cross classifying managerial and union strategies gives rise to the central theoretical contribution of this study: four social patterns of implementation and debugging each with implications for speed of rollout, efficiency improvements, and worker autonomy. First, a managerial forcing strategy and a developed union strategy produces a contested pattern of implementation characterised by a relatively slow rollout, limited efficiency gains, and limited but generalised worker upskilling. Second, a managerial forcing strategy in the absence of a developed union strategy results in a unilateral pattern of implementation associated with a rapid rollout, managerial satisficing on efficiency gains, and limited worker autonomy. Third, a co- ordinated pattern is the result of a developed union strategy in the context of managerial fostering and produces a steady rollout of the new technology with observable efficiency gains and high levels of worker autonomy. Finally, a co-opted pattern arises from managerial fostering in the absence of a developed union strategy with a rapid rollout, limited efficiency improvements, and isolated worker empowerment at managerial discretion.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".