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Record W2907345086 · doi:10.1177/0950017018817488

‘Off My Own Back’: Precarity on the Frontlines of Care Work

2019· article· en· W2907345086 on OpenAlexfundno aff
Donna Baines, Paul Kent, Sally Kent

Bibliographic record

VenueWork Employment and Society · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsnot available
FundersAustralian Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsPrecarityWorkforceCasualWork (physics)Care workMarketizationWorkfareShadow (psychology)Political sciencePublic relationsSociologyGender studiesPsychologyLawEngineering

Abstract

fetched live from OpenAlex

Hailed by some as representing the ‘most profound change in Australian disability history’, care work in the disability sector under the new National Disability Insurance Scheme is described by one frontline worker as ‘a massive swing towards a casual workforce and a massive cultural shock’. This firsthand account draws on 13 pages of unsolicited hand-written notes from a long-time, frontline care worker and his wife, as well as an in-depth interview and subsequent telephone and email conversations. The article gives voice to the experience of the frontline as disability workers grapple with almost complete casualization of their work, as the state retreats from its role in regulating employment and protecting workers in favour of the marketization of services and the advancing of the human rights of people with disabilities.

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)0.012
metaresearch head score (Gemma)0.025
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 score0.043
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.025
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0430.072
Scholarly communication0.0220.014
Open science0.0020.019
Research integrity0.0070.015
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.045
GPT teacher head0.321
Teacher spread0.276 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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