Bibliographic record
Abstract
This article explores the "ethical labour" of suspension––the conscious effort of deferring one's ethical judgement and reflections in order to avoid irreconcilable ethical conflicts between one's present activities and long-term goals. While people engage in ethical judgement and reflections in everyday social interactions, it is the laborious aspect of regulating one's ethical dispositions that I highlight in the concept of "ethical labour." Although it cannot be directly commodified, ethical labour is a form of labour as it consumes energy and is integral to the performance of other forms of labour, particularly intimate and emotional ones. This formulation of ethical labour draws on my long-term ethnographic research with a group of young women migrants working as hostesses in high-end nightclubs in southeast China. Many of them perform socially stigmatized work with the goal of contributing to their family and saving money for a dignified life in the future. Ethical labour is essential to their hostess work because it enables them to juggle multiple affective relationships and defer the fundamental ethical conflict. They express ethical labour through the phrase "to be a little more realistic," making sure that they obtain what they want at a particular moment. But ethical labour does not simply mean pushing ethical questions aside. It is sustained by conscious effort and is overshadowed by fears of ageing and failure to achieve long-term life goals. Prolonged ethical labour often fails to resolve ethical conflict and may intensify one's stress. My analysis of these women migrants' situation contributes to the sex-as-work debate regarding women's agency in work and their subjection to exploitation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".