Moral Projects and Compromise Resistance: Resisting Uncaring in Non-Profit Care Work
Bibliographic record
Abstract
Debates about workplace resistance and dissent are a central aspect of Labour Process Theory (Burawoy, 1979; Thompson, 1997; Thompson and Smith, 2017). This chapter focuses on care work in the non-profit social services (NPSS), and argues that resistance in this sector is a series of compromises and moral projects. With the objective of extending Labour Process Theory and commenting on resistance in the context of austerity, this chapter draws on qualitative data and on Ackroyd and Thompson's (1999; 2013) three-part analytic frame (described in more detail later) exploring the dynamics of resistance and dissent in care work. The NPSS is funded largely by government and grew dramatically under welfare state contracting out (Cunningham, 2008; Eikenberry, 2009). It provides support and care services to a range of populations such as the elderly, young, people with disabilities and people with mental health issues. Agencies come in a variety of sizes, from very small (one or two employees) to very large (1,000+ workers), and some operate as regional or national non-profit chains. Mirroring the contradictory relations of the austerity-linked, contracted-out state that funds most non-profit care in Canada, workplace resistance and dissent in the NPSS is an unstable equilibrium that can legitimize austerity, act as a catalyst to undermine it, or both. NPSS staff members often have altruistic/social justice identities outside of the workplace, and they seek these values in the organization's mission, work content and workplace relationships. Workers’ personal values often form the bedrock on which oppositional identities and practices are built, enhanced, frustrated and circumscribed, or a combination of all these factors simultaneously (Van Til, 2009; Nickel and Eikenberry, 2016). Most workers assume that altruistic values and identities will be held in common by fellow employees and managers, and seek ways to bond with others in the workplace around this presumed shared identity. In addition, they often educate and mobilize each other around ongoing political and social understandings of social justice and care (Aronson and Smith, 2010; 2011). This shared, oppositional analysis can extend beyond the workplace to analyses of the larger society and the ways that unjust relations are seen to be active in the workplace.
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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 | 0.031 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.028 | 0.105 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".