MétaCan
Menu
Back to cohort
Record W2156326146 · doi:10.1177/0022185613498664

Fragmented outcomes: International comparisons of gender, managerialism and union strategies in the nonprofit sector

2013· article· en· W2156326146 on OpenAlexaffabout
Donna Baines, Sara Charlesworth, Ian Cunningham

Bibliographic record

VenueJournal of Industrial Relations · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsManagerialismRestructuringPublic sectorWorkforcePrivate sectorPublic relationsGovernment (linguistics)Public administrationBusinessEconomic growthPolitical scienceEconomicsFinanceEconomy

Abstract

fetched live from OpenAlex

Since the mid-1980s, the nonprofit social services sector has been promoted as an option for cheaper and more flexible delivery of services. In order to comply with government standards and funding requirements, the sector has been subject to ongoing waves of restructuring and the introduction of new private market-like, outcomes-based management models, such as New Public Management. This article explores ways in which nonprofit social services sector workers experience their work as highly fragmented. Drawing on case studies completed as part of a larger project addressing restructuring in the nonprofit social services sector in Scotland, New Zealand, Australia and Canada, we examine three key aspects shaping work in the nonprofit social services sector: 1) workers’ experience of managerialism; 2) gendered strategies drawn on by workers in the agencies studied; and 3) union strategies in the nonprofit social services sector, as well as within individual workplaces. Conclusions focus on contributions to understanding managerialism as a strong but fragmented project in which even weak union presence and the willingness of the predominantly female workforce to sacrifice to provide care for others ensure that some level of social solidarity endures.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.332
Teacher spread0.255 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations39
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Industrial RelationsSame topicLabor Movements and UnionsFrench-language works237,207