Nonprofit Care Work as Social Glue: Creating and Sustaining Social Reproduction in the Context of Austerity/Late Neoliberalism
Bibliographic record
Abstract
This article will bring together the social glue concept of social reproduction and a feminist analysis of civil society to the study of nonprofit care work in order to cast analytic light on the dynamics of care work in the nonprofit sector and contribute to theorizing care work, to identify and theorize aspects of nonprofit care work which reproduce and sustain social glue, and to supplement theory on civil society. Drawing on qualitative interviews with nonprofit care workers in South Africa and Scotland, this article argues that care work, in general, and nonprofit care work, more specifically, are key components of civil society and central to the gendered social glue that holds societies together. We argue that nonprofit care workers are part a distinctive but porous set of social relations and have their own unique way of sustaining social bonds in the context of late neoliberalism. The article looks closely at three dynamics of social glue in nonprofit care work, namely, empowerment, emotional/personal costs, and unpaid work. We argue that nonprofit care workers find micro ways of resisting the erosion of social glue and reweaving the social fabric through care and relationship and further that these forms of resistance may sustain much needed social bonds until larger social transformation is possible.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.096 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".