Not Profiting from Precarity: The Work of Nonprofit Service Delivery and the Creation of Precariousness
Bibliographic record
Abstract
<p>This paper examines the impact of precarity on the nonprofit service providing sector (NPSS). Using in depth qualitative interviews, recent empirically-based surveys of the Ontario nonprofit sector and key academic and grey literature, we explore the deeper meaning of precarity in this sector. We contend that the NPSS is a unique, and in many respects, an ideal location in which to explore the workings and impact of precarity. Looking at the nonprofit sector reveals that precarity operates at various levels, the: 1) nonprofit labour force; 2) organization structure and operation of nonprofit agencies; and, 3) clients and communities serviced by these nonprofit organizations. By observing the workings of precarity in this sector, precarity is revealed to be far more than an employment based phenomenon but also a force that negatively impacts organizational structures as well as vulnerable communities.<br> </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".