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Record W1983313583 · doi:10.1177/0950017011419710

‘White knuckle care work’: violence, gender and new public management in the voluntary sector

2011· article· en· W1983313583 on OpenAlexaffabout
Donna Baines, Ian Cunningham

Bibliographic record

VenueWork Employment and Society · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScarcityCare workContext (archaeology)Profit (economics)Work (physics)Public relationsPolitical scienceEngineeringEconomics

Abstract

fetched live from OpenAlex

Drawing on comparative data from Canada and Scotland, this article explores reasons why violence is tolerated in non-profit care settings. This article provides insights into how the predominantly female (paid) workers’ orientations to work, the desire to care and the intrinsic rewards from working in a non-profit context interact with the organization of work and managerially constructed workplace norms and cultures to offset the tensions in an environment characterized by scarce resources and poor working conditions. This article also outlines how the same environment of scarce resources causes strains in management’s efforts to establish these cultures. Working with highly excluded service users, workers find themselves working at the edge of their endurance, beginning to participate in various forms of resistance, suggesting that even among the most highly committed, ‘white knuckle care’ may be unsustainable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.009
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.098
GPT teacher head0.313
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations71
Published2011
Admission routes2
Has abstractyes

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