Changing care? Men and managerialism in the nonprofit sector
Bibliographic record
Abstract
Summary The movement of men into care work in the predominantly female voluntary sector appears to be an unintended impact of welfare state contracting-out, managerialism and labour market restructuring. While not uniform, our comparative, international data (New Zealand and Scotland) show that some groups of men in nonprofit care work jobs embraced managerialism and used aspects of it to reshape and advance their work, while others undertook practices exemplifying a ‘caring masculinity’ more similar to practices currently associated with femininised ways of undertaking care activities. Findings Drawing on international comparative data collected as part of a larger study of restructuring in the nonprofit social services, this article suggests analytic clusters of masculinities operating in the voluntary sector and explores how the presence of men in care work may be changing it. The article also shows how hegemonic, masculinist-oriented practices in the workplace appear more amenable to managerialism than the expected feminine self-sacrificing, self-exploiting ethos of this highly gendered, female-majority sector. Applications These findings provide insights into the gendered and changing work in the nonprofit social services sector, and suggest ways the gender order is changing with the influx of male workers. The findings will be of interest to social work managers, supervisors, practitioners, policy analysts, students and educators.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".