The Emotional Economy of Care: Precarious Funding and Structural Violence in Canadian Non-Profit Social Services
Bibliographic record
Abstract
This dissertation explores the influence of funding dynamics within the non-profit social services (NPSS) sector serving seniors in Ottawa, Ontario, Canada, particularly focusing on how these dynamics contribute to gender-based structural violence against women workers. It utilizes a multi-disciplinary theoretical framework, integrating feminist political economy (FPE), affect theory, and theories of structural violence to analyze the intersection of gender, labour, and systemic challenges. Employing qualitative methods, including ethnographic fieldwork and 25 in-depth interviews conducted before and during the COVID-19 pandemic, the study provides a detailed examination of how pandemic-related policies have intensified pre-existing inequalities. I argue the NPSS’s precarious funding models, influenced by neoliberal policies, create conditions that can be understood as gender-based structural violence. I show how this form of violence is specific to the sector, stemming from the cyclical nature of funding which directly affects the material conditions and emotional demands of care work. The study uses concepts of structural violence, to help understand and theorize a cycle of funding in the sector with deep systemic roots. The analysis explores the material and emotional repercussions of these funding practices, revealing their profound impact on organizational, interpersonal, and care quality levels within the sector. Through affect theory, this dissertation articulates a “structure of feeling” that captures the collective emotional burden of workers, framed within an emotional economy of care. This funding cycle often exists alongside workers’ feelings of happiness, fulfillment, and meaningful work, driven by a profound sense of moral obligation, creating a bittersweet contradiction that helps understands why women stay in these challenging roles. Workers were seen to cling to the "cruel optimism" described by Berlant (2011), holding onto unattainable goals for significant change. However, women also display, what I call resilient optimism, engaging in collective care, resistance, and advocacy to manage and confront their difficult situations. This research calls for the restructuring of funding practices and workplace policies to enhance funding sustainability and equity, ensuring the sector can better support its predominantly women workforce, who are crucial to the delivery of essential community services.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".