Bibliographic record
Abstract
Abstract Part-Time for All offers solutions to four pressing problems: inequality for care-givers; family stress from demands of work and care; chronic time scarcity; and policy makers who are ignorant of care and care-givers with little access to policy making—the care/policy divide. Only a radical restructuring of both work and care can redress all these problems. We propose new norms: no one does paid work for more than 30 hours a week, and everyone contributes roughly 22 hours of unpaid care to family, friends, or their chosen community of care. Other approaches provide only partial solutions. For example, wages for housework, or excellent daycare, or flexible work hours would not overcome the care/policy divide. We explain why everyone needs to acquire the knowledge and dispositions that come from the sustained experience of providing care throughout one’s life. We show how work can be transformed to allow time for care giving, and how these new norms will generate a cultural shift in the value accorded care. While we focus primarily on human-to-human care, we include care for the earth. The final two chapters describe how these processes of transformation could be feasibly accomplished and why these changes are possible in high-income countries within our current global economy. Every one of our proposals already exists in at least one country; the task is to integrate the key reforms and scale them up. Given the magnitude of the current problems, deep changes are needed. Part-Time for All offers a feasible path forward.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.150 | 0.048 |
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".