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Record W2487025546 · doi:10.1057/9780230522831_3

Family Life, Moral Regulation and the State: Social Steering and the Personal Sphere

2003· book-chapter· en· W2487025546 on OpenAlexaboutno aff
John J. Rodger

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2003
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWelfare stateIgnoranceVeil of ignoranceWelfarePoliticsState (computer science)Social insurancePolitical scienceCivil societyFamily lifeSocial WelfarePublic administrationSociologyLawSocioeconomicsEconomic Justice

Abstract

fetched live from OpenAlex

The architects of the modern welfare state including Meidner and Rehn in Sweden, Laroque in France, Marsh in Canada, Van Rhijn in the Netherlands and Beveridge in Britain, had a vision of a society in which the collective resources of the state could be used to create a more just and humane social order (George, 1996). In Britain, Beveridge understood the welfare state project as the eradication of the five giants of idleness, want, disease, ignorance and squalor. He also believed that the instrument to accomplish that task was a welfare state which pooled risk through the principle of social insurance and channelled collective resources into the provision of free healthcare, education and affordable council housing in order to support family life. Part of the purpose behind welfare state intervention in social and family life has been to influence the choices that citizens make about their own and their children’s lives. The concept which will be used to examine this notion is social steering , action through which a social actor or social system is moved from one position to another by the intentional decisions of a political authority. The central issue to be examined is the degree to which control of the steering process in the field of social policy, particularly that area that deals with family life, is undermined by the transfer of responsibility for welfare from the state to civil society. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.023
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.002
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 head0.030
GPT teacher head0.259
Teacher spread0.229 · 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 designTheoretical or conceptual
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

Citations1
Published2003
Admission routes1
Has abstractyes

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