Productive Justice in the ‘Post‐Work Future’
Bibliographic record
Abstract
ABSTRACT Justice in production is concerned with ensuring the benefits and burdens of work are distributed in a way that is reflective of persons' status as moral equals. While a variety of accounts of productive justice have been offered, insufficient attention has been paid to the distribution of work's benefits and burdens in the future. In this article, after granting for the sake of argument forecasts of widespread future technological unemployment, we consider the implications this has for egalitarian requirements of productive justice. We argue that in relation to all the benefits affiliated with work, other than undertaking social contribution, the technological replacement of work is unproblematic as these benefits could in principle be attained elsewhere. But because social contribution uniquely corresponds to work (when work is understood as more than a paid job), the normative assessment of technological unemployment will turn on the value that theories of justice give to contributive activity. We then argue that despite technological replacement being plainly beneficial insofar as it relieves persons from the burdens of work, such as dangerous work or drudgery, because the nature of care work makes it less susceptible to technological replacement, egalitarian concern will require the burdens of care work to be shared equally between individuals.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".