Filling the gaps: Unpaid (and precarious) work in the nonprofit social services
Bibliographic record
Abstract
Unpaid work has long been used in nonprofit/voluntary social services to extend paid work. Drawing on three case studies of nonprofit social services in Canada, this article argues that due to austerity policies, the conditions for ‘pure’ gift relationships in unpaid social service work are increasingly rare. Instead, employers have found various ways to ‘fill the gaps’ in funding through the extraction of unpaid work in various forms. Precarious workers are highly vulnerable to expectations that they will ‘volunteer’ at their places of employment, while expectations that students will undertake unpaid internships is increasing the norm for degree completion and procurement of employment, and full-time workers often use unpaid work as a form of resistance. This article contributes to theory by advancing a spectrum of unpaid nonprofit social service work as compelled and coerced to varying degrees in the context of austerity policies and funding cutbacks.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.058 | 0.092 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.007 | 0.010 |
| 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".