Bringing life's work to market: Frontiers, framings, and frictions in marketised social reproduction
Bibliographic record
Abstract
The introduction to this theme issue discusses a series of papers examining the increasing marketisation of social reproduction and its effects on systems that sustain human and social life. This is done by examining the frontiers, framings, and frictions that arise when market systems are constructed to enable capital accumulation in the realm of social reproduction. Frontiers identify the expansion of market logic into new areas, framings explore how financial actors attempt to bring the logic of social reproduction within the purview of market competition, and frictions highlight the various tensions that generate resistance to the roll out of market logics. Through establishing these three areas, we argue that both market structures and systems of social reproduction should be understood as geographically variegated and, at times, uncertain. This variegation necessitates an understanding of marketised social reproduction as forged through complex articulations of market and non-market logics. Using cases from surrogacy to smart electricity meters, the papers in this theme issue illustrate that while these articulations may generate benefits for some individuals, households and communities, such processes of marketisation can introduce new layers of inequity and undermine the ethical relations and social commitments that sustain life—in the service of enabling accumulation.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".