Bad feeling at work: emotional labour, precarity, and the affective economy
Bibliographic record
Abstract
Returning to Arlie Hochschild’s foundational work, this article argues for the ongoing relevance of emotional labour in understanding the subjective demands placed on those working at the intersection of affective labour and precarity. Drawing on a range of feminist analyses, I understand emotional labour as the work entailed in producing profitable (often positive) affects at the level of the individual worker, thereby challenging views of affective labour that focus on the affects that circulate productively under neoliberalism. The stakes of such emotional labour in the affective economy, I argue, are heightened by conditions of labour precarity in which many workers are asked not only to produce positive affects, but also to subordinate the bad feelings that can arise alongside socio-economic insecurity. I understand the demand for positive affect from workers as emerging not only due to the productivity of such affects under neoliberalism, but also because the prevalence of positive feeling operates ideologically to normalize precarious working conditions. Bad feeling in this context threatens to challenge the neoliberal status quo. Drawing extensively on Tatjana Turanskyj’s 2011 film Eine Flexibe Frau, I identify the cultural and workplace logics by which bad feelings are excised and suppressed, primarily through the presumption of bad feeling as wilful. These logics complicate any effort to read a straightforward politics of resistance or refusal into bad feeling; however, I conclude that to view bad feeling as structurally embedded and functionalized within capitalist logics offers a means by which to respond differently to those who feel bad as we encounter them in the precarious affective economy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".