Bad feeling at work: emotional labour, precarity, and the affective economy
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
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| 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