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Record W2905349813 · doi:10.1177/0002764218816804

Self-Responsibility and Activation for Lone Mothers in the United Kingdom

2018· article· en· W2905349813 on OpenAlexaboutno aff
Jane Millar

Bibliographic record

VenueAmerican Behavioral Scientist · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWelfare reformQuarter (Canadian coin)Work (physics)Public relationsMoral responsibilitySociologyPolitical scienceWelfareLaw

Abstract

fetched live from OpenAlex

Lone mothers make up a quarter of all families with children in the United Kingdom and have been one of the key target groups for activation policies for the past two decades. In a relatively short period of time, the U.K. system has changed from treating lone mothers as carers to treating them as workers. Most lone mothers are now required to seek work, or to be in work, in order to be eligible for state support. These developments place self-responsibility at the center of welfare reform and paid work as the core of self-responsibility. The focus is very much on the individuals and their labor market obligations and downplays their social obligations, for example, to care for their children or other family members. The capacity to make choices about when and how much to engage in paid work is much reduced. This article explores what these developments have meant for lone mothers in the United Kingdom. The first main section outlines the key policy approaches and measures, highlighting the underpinning concepts of self-responsibility. The discussion also explores the experiences of lone mothers in relation to these policies, drawing on data from a long-term qualitative study. The second main section focuses on a new policy development—the introduction of Universal Credit—in which promoting an employment-based self-responsibility is unequivocally central to the policy aims and design.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
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 head0.079
GPT teacher head0.425
Teacher spread0.346 · 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.

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

Citations22
Published2018
Admission routes1
Has abstractyes

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