Re-imagining Parental Leave: a conceptual ‘thought experiment’
Bibliographic record
Abstract
This chapter is a conceptual, pragmatic and imaginative ‘thought experiment’. Broadly informed by Margaret Somers’ ‘historical sociology of concept formation’, which excavates the historicity, genealogies, and relationalities of concepts, we explore several key concepts, particularly commodification/decommodification and familialisation/defamilialisation. We argue that these concepts, and their histories and ensuing debates, are useful for thinking about and re-imagining Parental Leave as a social policy. The chapter begins by engaging with Gøsta Esping-Andersen’s contributions to debates on social policy and welfare states, briefly tracing his work’s roots in the work of Karl Polanyi, which we read with and through the writing of Fred Block and Margaret Somers, and Nancy Fraser. They and others argue that Polanyi’s work can help us understand and challenge relations between current enhanced conditions of neoliberal restructuring, market economies and ‘market fundamentalism’. With a focus on Canada, we map new pathways for future imaginaries in leave-to-care policy making. We argue that new interpretations of Polanyian insights introduce new conceptual configurations to Parental Leave debates, linking neoliberalism, paid work and care work, market fundamentalism, social protection, social citizenship, and entanglements between socio-economic rights and human rights.
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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 | 0.067 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.069 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".