Love and Work: Affect and Ideology Beyond ‘The Great Resignation’
Bibliographic record
Abstract
Taking the scene of ‘The Great Resignation’ in the USA and UK (2021‐2023) as its starting point, this paper explores how love ‐ with its promises and disappointments, its nurture and its destruction ‐ is activated in relation to the ideologies of work that prop up capitalism’s world. Through critical engagement with the popular maxims of ‘do what you love’ and ‘work won’t love you back’, we trace the weave of love and work in the context of predominantly (but not only) high-status, employment-based work within the unevenly gendered, racialised and sexualised labour markets in the USA and the UK. We show how the call to love work or to recognise work’s lack, while ostensibly antithetical, both offer a key to understanding the promise and problem of work’s love. We argue that work’s love is productive of the capitalist world and the violences that accompany it and foreclose alternative possibilities. Through a critique of Arendt’s theorisation of the world, we conclude by showing how love and work are central to geographical imaginaries of worldliness, and to both the rejection and possibility of other worlds after (or within) colonial-capitalism’s abolition. Our analysis thus demonstrates how affect and ideology ‐ that is, modes of feeling and forms of consciousness that (re)produce the material relations of capitalism’s world ‐ at once reverse into and continue one another in work’s love.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.060 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".