The demise of repetitive strain injury in sceptical governing rationalities of workplace managers
Bibliographic record
Abstract
In the mid-1990s, RSI (repetitive strain injury) loomed as an occupational health epidemic in industrialised countries. A decade later, the problem appears to have faded away, but there has been little explanation for how this problem might have diminished. This paper offers an explanation for the decline of RSI in the social relations of workplaces, in the pragmatic, day-to-day governance of occupational health by workplace managers. Using the conceptual lens of governmentality theory, this study examined how workplace managers conceptualised, rationalised and responded to RSI in their work organisations. Open-ended interviews were conducted with 35 managers at four Ontario newspaper workplaces. This paper describes managers as guided by a discourse of scepticism about the legitimacy of RSI complaints. This discourse was 'practised' in different ways at each newspaper in varied RSI-management strategies, but each strategy privileged ideas about RSI being problematic in certain types of workers rather than in types of physical work environments. This conceptualisation of RSI splintered, subordinated and collapsed the problem within a broad human resources framework. This paper suggests that the 'taming' of RSI might have occurred in the context of micro-political, workplace-level shifts in the conceptualisation and management of RSI as an occupational health problem.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.063 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".