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Record W3117219765 · doi:10.22215/etd/2017-11880

'It doesn’t always do justice to people’: Neoliberalism’s Reorganization of Social Service Delivery in Ontario

2017· dissertation· en· W3117219765 on OpenAlexaffabout
Theo Hug

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsCarleton University
FundersAustralian Government
KeywordsSocial workNeoliberalism (international relations)Public relationsRestructuringAusterityService delivery frameworkSociologyManagerialismSolidarityPublic administrationPolitical scienceService (business)BusinessPolitical economyPoliticsMarketingLaw

Abstract

fetched live from OpenAlex

Guided by institutional ethnography and labour process theory, this project explores the ways in which the discourses and practices of neoliberal austerity organize the work experiences of frontline social service providers and workers' various forms of resistance to this restructuring.Managerialist practices of accreditation and evidencebased practice appear to reorient service provision away from relational, social justice oriented work and community building, redistributing workers' time and energy to administrative practices involved in assessments, evaluations, and performance measurements.These ongoing changes take a toll on worker mental wellbeing and present challenges to the sustainability of social service provision by increasing workloads and limiting workers' access to support.While these changes in work processes present some challenges in workers' relationships with management and one another, they also open up new spaces to demonstrate solidarity and to work together to resist the neoliberalization of the sector. of Quality Assurance and chair of the Research Ethics Committee at [agency name and contact information].

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)0.005
metaresearch head score (Gemma)0.008
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 score0.199
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0320.030
Scholarly communication0.0070.003
Open science0.0020.007
Research integrity0.0020.002
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 head0.031
GPT teacher head0.357
Teacher spread0.326 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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