(Still) Up to No Good: Reconfiguring Worker Resistance and Misbehaviour in an Increasingly Unorganized World
Bibliographic record
Abstract
The way worker resistance and misbehaviour have been analyzed has undergone significant transformation over the past few decades. While researchers have observed the quantitative decline of formal or organized forms of industrial relations conflict, others have highlighted the emergence of informal and individualized (mis)behaviours. There have been a range of reasons advanced to explain both the decline in industrial disputes and in the lineal approaches to analyze workplace conflict. This article cautions the increasing tendency to analyze resistance and misbehaviour in an institutional vacuum. Drawing on longitudinal research across multiple organizational settings in Australia and Britain, the article identifies the longevity of institutional and structural factors to explain workplace behaviours, particularly among weakly organized workers. The evidence presented in this paper emphasizes the need to analyze employee resistance within its institutional context. The range of behaviours identified here in many non- or anti-union settings were shaped by the changing structural and institutional workplace regime: by sector, size, structure or managerial strategy (among others). By recognizing the importance of context and place, we argue that what is often portrayed as types of misbehaviour substitute for more assertive forms of resistance by workers who are vulnerable in the labour market or denied access to traditional collective structures of representation.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".